16.7.4.6.14 Human Action Recognition, Skeletal Representations

Shan, Y.H.[Yan-Hu], Zhang, Z.[Zhang], Yang, P.P.[Pei-Pei], Huang, K.Q.[Kai-Qi],
Adaptive Slice Representation for Human Action Classification,
CirSysVideo(25), No. 10, October 2015, pp. 1624-1636.
IEEE DOI 1511
BibRef
Earlier: A1, A2, A4, Only:
Learning Skeleton Stream Patterns with Slow Feature Analysis for Action Recognition,
Re-Id14(111-121).
Springer DOI 1504
feature extraction
See also Slow Feature Analysis for Human Action Recognition. BibRef

Shan, Y.H.[Yan-Hu], Zhang, Z.[Zhang], Zhang, J.G.[Jun-Ge], Huang, K.Q.[Kai-Qi], Wu, N.[Na], Hyun, O.S.[Oh Se],
Interest Point Selection with Spatio-temporal Context for Realistic Action Recognition,
AVSS12(94-99).
IEEE DOI 1211
BibRef

Zhou, Z.L.[Zhuo-Li], Song, M.L.[Ming-Li], Zhang, L.M.[Lu-Ming], Tao, D.C.[Da-Cheng], Bu, J.J.[Jia-Jun], Chen, C.[Chun],
kPose: A New Representation For Action Recognition,
ACCV10(III: 436-447).
Springer DOI 1011
BibRef

Shao, L.[Ling], Ji, L.[Ling], Liu, Y.[Yan], Zhang, J.G.[Jian-Guo],
Human action segmentation and recognition via motion and shape analysis,
PRL(33), No. 4, March 2012, pp. 438-445.
Elsevier DOI 1201
Human action segmentation; Motion analysis; PCOG; Motion history image; Human action recognition BibRef

Wu, D.[Di], Shao, L.[Ling],
Silhouette Analysis-Based Action Recognition Via Exploiting Human Poses,
CirSysVideo(23), No. 2, February 2013, pp. 236-243.
IEEE DOI 1301
BibRef
And:
Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition,
CVPR14(724-731)
IEEE DOI 1409

See also Deep Dynamic Neural Networks for Gesture Segmentation and Recognition. BibRef

Chaaraoui, A.A.[Alexandros Andre], Climent-Pérez, P.[Pau], Flórez-Revuelta, F.[Francisco],
Silhouette-based human action recognition using sequences of key poses,
PRL(34), No. 15, 2013, pp. 1799-1807.
Elsevier DOI 1309
Human action recognition BibRef

Chaaraoui, A.A.[Alexandros Andre], Padilla-Lopez, J.R., Flórez-Revuelta, F.[Francisco],
Fusion of Skeletal and Silhouette-Based Features for Human Action Recognition with RGB-D Devices,
CDC4CV13(91-97)
IEEE DOI 1403
feature extraction BibRef

Anwer, R.M.[Rao Muhammad], Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Laaksonen, J.T.[Jorma T.],
Top-Down Deep Appearance Attention for Action Recognition,
SCIA17(I: 297-309).
Springer DOI 1706
BibRef

Khan, F.S.[Fahad Shahbaz], Anwer, R.M.[Rao Muhammad], van de Weijer, J.[Joost], Felsberg, M.[Michael], Laaksonen, J.T.[Jorma T.],
Deep Semantic Pyramids for Human Attributes and Action Recognition,
SCIA15(341-353).
Springer DOI 1506
BibRef

Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Anwer, R.M.[Rao Muhammad], Felsberg, M.[Michael], Gatta, C.,
Semantic Pyramids for Gender and Action Recognition,
IP(23), No. 8, August 2014, pp. 3633-3645.
IEEE DOI 1408
computer vision BibRef

Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Anwer, R.M.[Rao Muhammad], Bagdanov, A.D.[Andrew D.], Felsberg, M.[Michael], Laaksonen, J.T.[Jorma T.],
Scale coding bag of deep features for human attribute and action recognition,
MVA(29), No. 1, January 2018, pp. 55-71.
Springer DOI 1801
BibRef
Earlier: A1, A2, A4, A5, Only:
Scale Coding Bag-of-Words for Action Recognition,
ICPR14(1514-1519)
IEEE DOI 1412
Encoding BibRef

Khan, F.S.[Fahad Shahbaz], Xu, J.L.[Jiao-Long], van de Weijer, J.[Joost], Bagdanov, A.D.[Andrew D.], Anwer, R.M., Lopez, A.M.,
Recognizing Actions Through Action-Specific Person Detection,
IP(24), No. 11, November 2015, pp. 4422-4432.
IEEE DOI 1509
computer vision BibRef

Ofli, F.[Ferda], Chaudhry, R.[Rizwan], Kurillo, G.[Gregorij], Vidal, R.[René], Bajcsy, R.[Ruzena],
Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition,
JVCIR(25), No. 1, 2014, pp. 24-38.
Elsevier DOI 1502
BibRef
Earlier: HAU3D12(8-13).
IEEE DOI 1207
Human action representation BibRef

Pazhoumand-Dar, H.[Hossein], Lam, C.P.[Chiou-Peng], Masek, M.[Martin],
Joint movement similarities for robust 3D action recognition using skeletal data,
JVCIR(30), No. 1, 2015, pp. 10-21.
Elsevier DOI 1507
Human action recognition BibRef

Amor, B.B., Su, J., Srivastava, A.,
Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories,
PAMI(38), No. 1, January 2016, pp. 1-13.
IEEE DOI 1601
Hidden Markov models BibRef

Cai, X., Zhou, W., Wu, L., Luo, J., Li, H.,
Effective Active Skeleton Representation for Low Latency Human Action Recognition,
MultMed(18), No. 2, February 2016, pp. 141-154.
IEEE DOI 1601
Acceleration BibRef

Azis, N.A., Jeong, Y.S., Choi, H.J., Iraqi, Y.,
Weighted averaging fusion for multi-view skeletal data and its application in action recognition,
IET-CV(10), No. 2, 2016, pp. 134-142.
DOI Link 1603
feature extraction BibRef

Du, Y.[Yong], Fu, Y., Wang, L.[Liang],
Representation Learning of Temporal Dynamics for Skeleton-Based Action Recognition,
IP(25), No. 7, July 2016, pp. 3010-3022.
IEEE DOI 1606
bone BibRef

Si, C.Y.[Chen-Yang], Jing, Y.[Ya], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network,
PR(107), 2020, pp. 107511.
Elsevier DOI 2008
BibRef
Earlier:
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning,
ECCV18(I: 106-121).
Springer DOI 1810
Skeleton-based action recognition, Hierarchical spatial reasoning, Temporal stack learning, Clip-based incremental loss BibRef

Si, C.Y.[Chen-Yang], Chen, W.T.[Wen-Tao], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition,
CVPR19(1227-1236).
IEEE DOI 2002
BibRef

Song, Y.F.[Yi-Fan], Zhang, Z.[Zhang], Shan, C.[Caifeng], Wang, L.[Liang],
Richly Activated Graph Convolutional Network for Robust Skeleton-Based Action Recognition,
CirSysVideo(31), No. 5, 2021, pp. 1915-1925.
IEEE DOI 2105
BibRef
Earlier: A1, A2, A4, Only:
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons,
ICIP19(1-5)
IEEE DOI 1910
Action Recognition, Skeleton Data, Graph Convolutional Network, Activation Maps, Occlusion BibRef

Jing, Y.[Ya], Wang, J.[Junbo], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Relational graph neural network for situation recognition,
PR(108), 2020, pp. 107544.
Elsevier DOI 2008
Situation recognition, Relationship modeling, Graph neural network, Reinforcement learning BibRef

Du, Y.[Yong], Wang, W.[Wei], Wang, L.[Liang],
Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition,
CVPR15(1110-1118)
IEEE DOI 1510
BibRef

Wang, H.S.[Hong-Song], Wang, L.[Liang],
Beyond Joints: Learning Representations From Primitive Geometries for Skeleton-Based Action Recognition and Detection,
IP(27), No. 9, September 2018, pp. 4382-4394.
IEEE DOI 1807
feature extraction, image classification, image motion analysis, image representation, learning (artificial intelligence), viewpoint transformation BibRef

Wang, P.[Peng], Wen, J.[Jun], Si, C.Y.[Chen-Yang], Qian, Y.T.[Yun-Tao], Wang, L.[Liang],
Contrast-Reconstruction Representation Learning for Self-Supervised Skeleton-Based Action Recognition,
IP(31), 2022, pp. 6224-6238.
IEEE DOI 2210
Skeleton, Dynamics, Representation learning, Image reconstruction, Task analysis, Computational modeling, Visualization, contrastive learning BibRef

Jung, H.J.[Hyun-Joo], Hong, K.S.[Ki-Sang],
Modeling temporal structure of complex actions using Bag-of-Sequencelets,
PRL(85), No. 1, 2017, pp. 21-28.
Elsevier DOI 1612
BibRef
Earlier:
Enhanced Sequence Matching for Action Recognition from 3D Skeletal Data,
ACCV14(V: 226-240).
Springer DOI 1504
Action recognition BibRef

Jung, H.J.[Hyun-Joo], Hong, K.S.[Ki-Sang],
Versatile Model for Activity Recognition: Sequencelet Corpus Model,
FG18(325-332)
IEEE DOI 1806
Activity recognition, Indexes, Semantics, Support vector machines, Task analysis, Training, Training data, activity recognition, sequencelet BibRef

Qiao, R.Z.[Rui-Zhi], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition,
PR(66), No. 1, 2017, pp. 202-212.
Elsevier DOI 1704
Action recognition BibRef

Liu, M.Y.[Meng-Yuan], Liu, H.[Hong], Chen, C.[Chen],
Enhanced skeleton visualization for view invariant human action recognition,
PR(68), No. 1, 2017, pp. 346-362.
Elsevier DOI 1704
Human action recognition BibRef

Hu, L.Z.[Li-Zhang], Xu, J.H.[Jin-Hua],
Learning Discriminative Representation for Skeletal Action Recognition Using LSTM Networks,
CAIP17(II: 94-104).
Springer DOI 1708
BibRef

Weng, J., Weng, C., Yuan, J.,
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action Recognition,
CVPR17(445-454)
IEEE DOI 1711
Benchmark testing, Image recognition, Pattern recognition, Skeleton, Videos BibRef

Rahmani, H., Mian, A., Shah, M.,
Learning a Deep Model for Human Action Recognition from Novel Viewpoints,
PAMI(40), No. 3, March 2018, pp. 667-681.
IEEE DOI 1802
Knowledge transfer, Solid modeling, Training, Trajectory, Videos, Cross-view, view knowledge transfer BibRef

Rahmani, H., Bennamoun, M.,
Learning Action Recognition Model from Depth and Skeleton Videos,
ICCV17(5833-5842)
IEEE DOI 1802
human computer interaction, image motion analysis, image representation, image sensors, Videos BibRef

Cao, C.Q.[Cong-Qi], Zhang, Y.F.[Yi-Fan], Zhang, C., Lu, H.Q.[Han-Qing],
Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition,
Cyber(48), No. 3, March 2018, pp. 1095-1108.
IEEE DOI 1802
Convolution, Estimation, Feature extraction, Kernel, Optical imaging, Skeleton, Trajectory, Action recognition, body joints, two-stream bilinear model BibRef

Li, C.L.[Chao-Long], Cui, Z.[Zhen], Zheng, W.M.[Wen-Ming], Xu, C.Y.[Chun-Yan], Ji, R.R.[Rong-Rong], Yang, J.[Jian],
Action-Attending Graphic Neural Network,
IP(27), No. 7, July 2018, pp. 3657-3670.
IEEE DOI 1805
Dynamics, Feature extraction, Hidden Markov models, Joints, Neural networks, skeleton-based action recognition BibRef

Li, R.[Rui], Liu, Z.Y.[Zhen-Yu], Tan, J.R.[Jian-Rong],
Human motion segmentation using collaborative representations of 3D skeletal sequences,
IET-CV(12), No. 4, June 2018, pp. 434-442.
DOI Link 1805
BibRef

Wang, H.S.[Hong-Song], Wang, L.[Liang],
Learning content and style: Joint action recognition and person identification from human skeletons,
PR(81), 2018, pp. 23-35.
Elsevier DOI 1806
Content and style, Action recognition, Person identification from motions, Skeleton transformation, Multi-task RNN BibRef

Chang, J.Y.[Ju Yong], Heo, Y.S.[Yong Seok],
Data Augmented Dynamic Time Warping for Skeletal Action Classification,
IEICE(E101-D), No. 6, June 2018, pp. 1562-1571.
WWW Link. 1806
BibRef

Pham, H.H.[Huy-Hieu], Khoudour, L.[Louahdi], Crouzil, A.[Alain], Zegers, P.[Pablo], Velastin, S.A.[Sergio A.],
Exploiting deep residual networks for human action recognition from skeletal data,
CVIU(170), 2018, pp. 51-66.
Elsevier DOI 1806
3D Action recognition, Deep residual networks, Skeletal data BibRef

Pham, H.H.[Huy Hieu], Salmane, H.[Houssam], Khoudour, L.[Louahdi], Crouzil, A.[Alain], Zegers, P.[Pablo], Velastin, S.A.[Sergio A.],
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data,
ICIAR19(I:18-32).
Springer DOI 1909
BibRef

Xu, Y.Y.[Yang-Yang], Cheng, J.[Jun], Wang, L.[Lei], Xia, H.Y.[Hai-Ying], Liu, F.[Feng], Tao, D.P.[Da-Peng],
Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition,
SPLetters(25), No. 7, July 2018, pp. 1044-1048.
IEEE DOI 1807
bone, convolution, feature extraction, image motion analysis, image recognition, learning (artificial intelligence), skeleton BibRef

Papadopoulos, G.T.[Georgios T.], Daras, P.[Petros],
Human Action Recognition Using 3D Reconstruction Data,
CirSysVideo(28), No. 8, August 2018, pp. 1807-1823.
IEEE DOI 1808
BibRef
Earlier:
Local descriptions for human action recognition from 3D reconstruction data,
ICIP14(2814-2818)
IEEE DOI 1502
Shape, Feature extraction, Robustness, Estimation, Histograms, Videos, 3D flow, 3D reconstruction, 3D shape, action recognition. BibRef

Papadopoulos, G.T.[Georgios T.], Axenopoulos, A.[Apostolos], Daras, P.[Petros],
Real-Time Skeleton-Tracking-Based Human Action Recognition Using Kinect Data,
MMMod14(I: 473-483).
Springer DOI 1405
BibRef

Zhang, Y.[Yong], Shen, B.W.[Bo-Wei], Wang, S.F.[Shao-Fan], Kong, D.[Dehui], Yin, B.C.[Bao-Cai],
L0-regularization-based skeleton optimization from consecutive point sets of kinetic human body,
PandRS(143), 2018, pp. 124-133.
Elsevier DOI 1808
minimization, Skeleton optimization, Consecutive point sets, Kinetic human body BibRef

Zhang, S.Y.[Song-Yang], Yang, Y.[Yang], Xiao, J.[Jun], Liu, X.M.[Xiao-Ming], Yang, Y.[Yi], Xie, D.[Di], Zhuang, Y.T.[Yue-Ting],
Fusing Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks,
MultMed(20), No. 9, September 2018, pp. 2330-2343.
IEEE DOI 1809
BibRef
Earlier: A1, A4, A3, Only:
On Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks,
WACV17(148-157)
IEEE DOI 1609
feature extraction, image recognition, optimisation, recurrent neural nets, recurrent neural network models, score fusion. Computational modeling, Logic gates, Neurons, Nonhomogeneous media, Skeleton. BibRef

Boulahia, S.Y.[Said Yacine], Anquetil, E.[Eric], Multon, F.[Franck], Kulpa, R.[Richard],
CuDi3D: Curvilinear displacement based approach for online 3D action detection,
CVIU(174), 2018, pp. 57-69.
Elsevier DOI 1812
BibRef
Earlier: A1, A2, A4, A3:
HIF3D: Handwriting-Inspired Features for 3D skeleton-based action recognition,
ICPR16(985-990)
IEEE DOI 1705
Online action recognition, Skeleton-based approach, Human action detection, Curvilinear displacement, Skeleton data stream. Feature extraction, Handwriting recognition, HIF3D, Handwriting-Inspired Features, Human action recognition, Joint trajectory modelling, RGB-D data, Skeleton-based, features BibRef

Min, W.D.[Wei-Dong], Yao, L.Y.[Lei-Yue], Lin, Z.R.[Zhen-Rong], Liu, L.[Li],
Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle,
IET-CV(12), No. 8, December 2018, pp. 1133-1140.
DOI Link 1812
BibRef

Ghorbel, E.[Enjie], Boonaert, J.[Jacques], Boutteau, R.[Rémi], Lecoeuche, S.[Stéphane], Savatier, X.[Xavier],
An extension of kernel learning methods using a modified Log-Euclidean distance for fast and accurate skeleton-based Human Action Recognition,
CVIU(175), 2018, pp. 32-43.
Elsevier DOI 1812
Kernel methods, Symmetric positive semi-definite matrices, Human action recognition, SVM, Covariance matrices, Log-Euclidean distance BibRef

Weng, J., Weng, C., Yuan, J., Liu, Z.,
Discriminative Spatio-Temporal Pattern Discovery for 3D Action Recognition,
CirSysVideo(29), No. 4, April 2019, pp. 1077-1089.
IEEE DOI 1904
Mutual information, Pattern recognition, Solid modeling, Skeleton, Target recognition, discriminative skeleton-based action recognition BibRef

Pham, H.H.[Huy-Hieu], Khoudour, L.[Louahdi], Crouzil, A.[Alain], Zegers, P.[Pablo], Velastin, S.A.[Sergio A.],
Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks,
IET-CV(13), No. 3, April 2019, pp. 319-328.
DOI Link 1904
BibRef
Earlier:
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks,
ICIP18(3483-3487)
IEEE DOI 1809
Skeleton, Image color analysis, Training, Task analysis, Hidden Markov models, Feature extraction, CNNs BibRef

Cavazza, J.[Jacopo], Morerio, P.[Pietro], Murino, V.[Vittorio],
Scalable and compact 3D action recognition with approximated RBF kernel machines,
PR(93), 2019, pp. 25-35.
Elsevier DOI 1906
Kernel machines, Kernel approximation, Action recognition, Skeletal joints, Covariance representation BibRef

Men, Q.H.[Qian-Hui], Leung, H.[Howard],
Retrieval of spatial-temporal motion topics from 3D skeleton data,
VC(35), No. 6-8, June 2018, pp. 973-984.
WWW Link. 1906
BibRef

Zhang, P.F.[Peng-Fei], Lan, C.L.[Cui-Ling], Xing, J.L.[Jun-Liang], Zeng, W.J.[Wen-Jun], Xue, J.R.[Jian-Ru], Zheng, N.N.[Nan-Ning],
View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition,
PAMI(41), No. 8, August 2019, pp. 1963-1978.
IEEE DOI 1907
Skeleton, Adaptation models, Adaptive systems, Recurrent neural networks, Cameras, consistent BibRef

Zhang, P.F.[Peng-Fei], Xue, J.R.[Jian-Ru], Lan, C.L.[Cui-Ling], Zeng, W.J.[Wen-Jun], Gao, Z.N.[Zhan-Ning], Zheng, N.N.[Nan-Ning],
Adding Attentiveness to the Neurons in Recurrent Neural Networks,
ECCV18(IX: 136-152).
Springer DOI 1810
BibRef

Zhang, P.F.[Peng-Fei], Lan, C.L.[Cui-Ling], Xing, J.L.[Jun-Liang], Zeng, W.J.[Wen-Jun], Xue, J.R.[Jian-Ru], Zheng, N.N.[Nan-Ning],
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data,
ICCV17(2136-2145)
IEEE DOI 1802
image motion analysis, image recognition, recurrent neural nets, 3D skeleton data, LSTM architecture, BibRef

Nie, Q., Wang, J., Wang, X., Liu, Y.,
View-Invariant Human Action Recognition Based on a 3D Bio-Constrained Skeleton Model,
IP(28), No. 8, August 2019, pp. 3959-3972.
IEEE DOI 1907
bone, feature extraction, gesture recognition, image motion analysis, image recognition, image representation, bio-constrained skeleton model BibRef

Meng, F., Liu, H., Liang, Y., Tu, J., Liu, M.,
Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition,
IP(28), No. 11, November 2019, pp. 5281-5295.
IEEE DOI 1909
Skeleton, Training, Testing, Deep learning, Transforms, Neural networks, Task analysis, Human action recognition, LSTM BibRef

Tu, J., Liu, H., Meng, F., Liu, M., Ding, R.,
Spatial-Temporal Data Augmentation Based on LSTM Autoencoder Network for Skeleton-Based Human Action Recognition,
ICIP18(3478-3482)
IEEE DOI 1809
Skeleton, Training, Data models, Decoding, Neurons, Protocols, 3D Action Recognition, Long Short-Term Memory, Autoencoder BibRef

Li, Q.M.[Qi-Ming], Lin, W.X.[Wen-Xiong], Li, J.[Jun],
Human activity recognition using dynamic representation and matching of skeleton feature sequences from RGB-D images,
SP:IC(68), 2018, pp. 265-272.
Elsevier DOI 1810
Human activity recognition, Dynamic representation and matching, Shape dynamic time warping BibRef

Yang, Z.Y.[Zheng-Yuan], Li, Y.C.[Yun-Cheng], Yang, J.C.[Jian-Chao], Luo, J.B.[Jie-Bo],
Action Recognition With Spatio-Temporal Visual Attention on Skeleton Image Sequences,
CirSysVideo(29), No. 8, August 2019, pp. 2405-2415.
IEEE DOI 1908
BibRef
Earlier:
Action Recognition with Visual Attention on Skeleton Images,
ICPR18(3309-3314)
IEEE DOI 1812
Skeleton, Visualization, Optical imaging, Image recognition, Image sequences, visual attention. Pattern recognition, Semantics BibRef

Wei, P., Sun, H., Zheng, N.,
Learning Composite Latent Structures for 3D Human Action Representation and Recognition,
MultMed(21), No. 9, September 2019, pp. 2195-2208.
IEEE DOI 1909
Skeleton, Semantics, Hidden Markov models, Deep learning, Sun, Solid modeling, composite latent structure BibRef

Ke, Q., Bennamoun, M., Rahmani, H., An, S., Sohel, F.A., Boussaid, F.,
Learning Latent Global Network for Skeleton-Based Action Prediction,
IP(29), No. 1, 2020, pp. 959-970.
IEEE DOI 1910
Skeleton, Videos, Australia, Recurrent neural networks, Lighting, Video sequences, convolutional neural networks BibRef

Cao, C.Q.[Cong-Qi], Lan, C.L.[Cui-Ling], Zhang, Y.F.[Yi-Fan], Zeng, W.J.[Wen-Jun], Lu, H.Q.[Han-Qing], Zhang, Y.N.[Yan-Ning],
Skeleton-Based Action Recognition With Gated Convolutional Neural Networks,
CirSysVideo(29), No. 11, November 2019, pp. 3247-3257.
IEEE DOI 1911
Skeleton, Logic gates, Task analysis, Recurrent neural networks, Matrix converters, convolutional neural networks
See also Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks with Spatiotemporal Transformer Modules. BibRef

Shi, L.[Lei], Zhang, Y.F.[Yi-Fan], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Decoupled Spatial-temporal Attention Network for Skeleton-based Action-gesture Recognition,
ACCV20(V:38-53).
Springer DOI 2103
BibRef

Cheng, K.[Ke], Zhang, Y.F.[Yi-Fan], He, X.Y.[Xiang-Yu], Chen, W.H.[Wei-Han], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Skeleton-Based Action Recognition With Shift Graph Convolutional Network,
CVPR20(180-189)
IEEE DOI 2008
Skeleton, Kernel, Convolutional codes, Computational modeling, Adaptation models, Pattern recognition BibRef

Shi, L.[Lei], Zhang, Y.F.[Yi-Fan], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks,
IP(29), 2020, pp. 9532-9545.
IEEE DOI 2011
BibRef
Earlier:
Skeleton-Based Action Recognition With Directed Graph Neural Networks,
CVPR19(7904-7913).
IEEE DOI 2002
BibRef
And:
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition,
CVPR19(12018-12027).
IEEE DOI 2002
Adaptation models, Joints, Data models, Bones, Spatiotemporal phenomena, Task analysis, multi-stream network BibRef

Shi, L.[Lei], Zhang, Y.F.[Yi-Fan], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Action recognition via pose-based graph convolutional networks with intermediate dense supervision,
PR(121), 2022, pp. 108170.
Elsevier DOI 2109
Action recognition, Skeleton BibRef

Cheng, K.[Ke], Zhang, Y.F.[Yi-Fan], He, X.Y.[Xiang-Yu], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Extremely Lightweight Skeleton-Based Action Recognition with ShiftGCN++,
IP(30), 2021, pp. 7333-7348.
IEEE DOI 2108
Skeleton, Convolutional codes, Image recognition, Computational modeling, Adaptation models, shift network BibRef

Cheng, K.[Ke], Zhang, Y.F.[Yi-Fan], Cao, C.Q.[Cong-Qi], Shi, L.[Lei], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Decoupling GCN with Dropgraph Module for Skeleton-based Action Recognition,
ECCV20(XXIV:536-553).
Springer DOI 2012
BibRef

Zhu, K.J.[Kai-Jun], Wang, R.X.[Ru-Xin], Zhao, Q.S.[Qing-Song], Cheng, J.[Jun], Tao, D.P.[Da-Peng],
A Cuboid CNN Model with an Attention Mechanism for Skeleton-Based Action Recognition,
MultMed(22), No. 11, November 2020, pp. 2977-2989.
IEEE DOI 2010
Feature extraction, Skeleton, Sensors, Spatiotemporal phenomena, Hidden Markov models, Neural networks, feature cuboid BibRef

Ghazal, S.[Sumaira], Khan, U.S.[Umar S.], Saleem, M.M.[Muhammad Mubasher], Rashid, N.[Nasir], Iqbal, J.[Javaid],
Human activity recognition using 2D skeleton data and supervised machine learning,
IET-IPR(13), No. 13, November 2019, pp. 2572-2578.
DOI Link 1911
BibRef

Liu, J.[Jun], Ding, H.H.[Heng-Hui], Shahroudy, A.[Amir], Duan, L.Y.[Ling-Yu], Jiang, X.D.[Xu-Dong], Wang, G.[Gang], Kot, A.C.[Alex C.],
Feature Boosting Network For 3D Pose Estimation,
PAMI(42), No. 2, February 2020, pp. 494-501.
IEEE DOI 2001
Pose estimation, Boosting, Logic gates, Reliability, context consistency gate BibRef

Liu, J.[Jun], Shahroudy, A.[Amir], Xu, D.[Dong], Kot, A.C., Wang, G.[Gang],
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates,
PAMI(40), No. 12, December 2018, pp. 3007-3021.
IEEE DOI 1811
BibRef
Earlier: A1, A2, A3, A5, Only:
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition,
ECCV16(III: 816-833).
Springer DOI 1611
Recurrent neural networks, Spatiotemporal phenomena, Feature extraction, skeleton sequence BibRef

Liu, J.H.[Jia-Heng], Xu, D.[Dong],
GeometryMotion-Net: A Strong Two-Stream Baseline for 3D Action Recognition,
CirSysVideo(31), No. 12, December 2021, pp. 4711-4721.
IEEE DOI 2112
Feature extraction, Geometry, Cloud computing, Skeleton, Data mining, Deep learning, Point cloud, two-stream BibRef

Liu, J.[Jun], Shahroudy, A.[Amir], Wang, G.[Gang], Duan, L.Y.[Ling-Yu], Kot, A.C.[Alex C.],
Skeleton-Based Online Action Prediction Using Scale Selection Network,
PAMI(42), No. 6, June 2020, pp. 1453-1467.
IEEE DOI 2005
Skeleton, Task analysis, Videos, Real-time systems, Pattern recognition, skeleton data BibRef

Liu, J.[Jun], Shahroudy, A.[Amir], Wang, G.[Gang], Duan, L.Y.[Ling-Yu], Kot, A.C.[Alex C.],
SSNet: Scale Selection Network for Online 3D Action Prediction,
CVPR18(8349-8358)
IEEE DOI 1812
Convolution, Skeleton, Task analysis, Predictive models, Real-time systems BibRef

Qin, Y.[Yang], Mo, L.F.[Ling-Fei], Li, C.Y.[Chen-Yang], Luo, J.Y.[Jia-Yi],
Skeleton-Based Action Recognition by Part-Aware Graph Convolutional Networks,
VC(36), No. 3, March 2020, pp. 621-631.
WWW Link. 2002
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Ding, W.W.[Wen-Wen], Li, X.[Xiao], Li, G.[Guang], Wei, Y.S.[Yue-Song],
Global relational reasoning with spatial temporal graph interaction networks for skeleton-based action recognition,
SP:IC(83), 2020, pp. 115776.
Elsevier DOI 2003
Deep learning, Graph convolutional network, Convolutional neural networks, Spatio-temporal graph, Message passing BibRef

Ding, W.W.[Wen-Wen], Zhou, G.H.[Guang-Hui], Ding, C.Y.[Chong-Yang], Li, G.[Guang], Liu, K.[Kai],
Graph-based relational reasoning in a latent space for skeleton-based action recognition,
JVCIR(83), 2022, pp. 103410.
Elsevier DOI 2202
Deep learning, Graph neural networks, Graph convolutional network, Message passing, Grassmannian geometry BibRef

Franco, A.[Annalisa], Magnani, A.[Antonio], Maio, D.[Dario],
A multimodal approach for human activity recognition based on skeleton and RGB data,
PRL(131), 2020, pp. 293-299.
Elsevier DOI 2004
Human activity recognition, Kinect sensor, Temporal images BibRef

Brighi, M.[Marco], Franco, A.[Annalisa], Maio, D.[Dario],
ActivityExplorer: A semi-supervised approach to discover unknown activity classes in HAR systems,
PRL(151), 2021, pp. 340-347.
Elsevier DOI 2110
Human Activity Recognition, Semi-supervised learning, Metric Learning BibRef

Franco, A.[Annalisa], Magnani, A.[Antonio], Maio, D.[Dario],
Joint Orientations from Skeleton Data for Human Activity Recognition,
CIAP17(I:152-162).
Springer DOI 1711
BibRef

Li, G.[Gang], Li, C.Y.[Chun-Yu],
Learning skeleton information for human action analysis using Kinect,
SP:IC(84), 2020, pp. 115814.
Elsevier DOI 2004
Human action recognition, Kinect sensor, Depth image, Human skeleton information BibRef

Li, Y.S.[Yan-Shan], Xia, R.J.[Rong-Jie], Liu, X.[Xing],
Learning shape and motion representations for view invariant skeleton-based action recognition,
PR(103), 2020, pp. 107293.
Elsevier DOI 2005
Human action recognition, Skeleton sequence, Representation learning, View invariant, Geometric Algebra BibRef

Ghorbel, E., Demisse, G., Aouada, D., Ottersten, B.,
Fast Adaptive Reparametrization (FAR) With Application to Human Action Recognition,
SPLetters(27), 2020, pp. 580-584.
IEEE DOI 2005
Manifolds, Skeleton, Algebra, Optimization, Benchmark testing, Shape, Feature extraction, Reparametrization, action recognition Riemannian manifolds BibRef

Li, J.A.[Jian-An], Xie, X.M.[Xue-Mei], Pan, Q.Z.[Qing-Zhe], Cao, Y.[Yuhan], Zhao, Z.[Zhifu], Shi, G.M.[Guang-Ming],
SGM-Net: Skeleton-guided multimodal network for action recognition,
PR(104), 2020, pp. 107356.
Elsevier DOI 2005
Action recognition, multi-modality, skeleton-guided BibRef

Kawamura, K.[Kazuki], Matsubara, T.[Takashi], Uehara, K.[Kuniaki],
Deep State-Space Model for Noise Tolerant Skeleton-Based Action Recognition,
IEICE(E103-D), No. 6, June 2020, pp. 1217-1225.
WWW Link. 2006
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Cho, S.[Sangwoo], Maqbool, M.H.[Muhammad Hasan], Liu, F.[Fei], Foroosh, H.[Hassan],
Self-Attention Network for Skeleton-based Human Action Recognition,
WACV20(624-633)
IEEE DOI 2006
Skeleton, Semantics, Data mining, Recurrent neural networks, Computational modeling, Encoding BibRef

Li, S., Jiang, T., Huang, T., Tian, Y.,
Global Co-occurrence Feature Learning and Active Coordinate System Conversion for Skeleton-based Action Recognition,
WACV20(575-583)
IEEE DOI 2006
Skeleton, Feature extraction, Convolution, Solid modeling, Recurrent neural networks, Head BibRef

Zhu, G.M.[Guang-Ming], Zhang, L.[Liang], Li, H.S.[Hong-Sheng], Shen, P.[Peiyi], Shah, S.A.A.[Syed Afaq Ali], Bennamoun, M.[Mohammed],
Topology-Learnable Graph Convolution for Skeleton-Based Action Recognition,
PRL(135), 2020, pp. 286-292.
Elsevier DOI 2006
Action recognition, Graph convolution, Graph topology, Skeleton BibRef

Zhu, G.M.[Guang-Ming], Yang, L.[Lu], Zhang, L.[Liang], Shen, P.[Peiyi], Song, J.[Juan],
Recurrent Graph Convolutional Networks for Skeleton-based Action Recognition,
ICPR21(1352-1359)
IEEE DOI 2105
Deep learning, Network topology, Topology, Pattern recognition BibRef

Jiang, X., Xu, K., Sun, T.,
Action Recognition Scheme Based on Skeleton Representation With DS-LSTM Network,
CirSysVideo(30), No. 7, July 2020, pp. 2129-2140.
IEEE DOI 2007
Skeleton, Hidden Markov models, Noise reduction, Geometry, Robustness, Electrical engineering, STAE BibRef

Guo, H.J.[Hong-Ji], Ren, Z.[Zhou], Wu, Y.[Yi], Hua, G.[Gang], Ji, Q.[Qiang],
Uncertainty-Based Spatial-Temporal Attention for Online Action Detection,
ECCV22(IV:69-86).
Springer DOI 2211
BibRef

Cui, R.[Ran], Zhu, A.[Aichun], Wu, J.R.[Jing-Ran], Hua, G.[Gang],
Skeleton-based attention-aware spatial-temporal model for action detection and recognition,
IET-CV(14), No. 5, August 2020, pp. 177-184.
DOI Link 2007
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Cui, R.[Ran], Hua, G.[Gang], Wu, J.R.[Jing-Ran],
AP-GAN: Predicting skeletal activity to improve early activity recognition,
JVCIR(73), 2020, pp. 102923.
Elsevier DOI 2012
Early activity recognition, Activity prediction, Skeleton, GAN BibRef

Liu, X.[Xing], Li, Y.S.[Yan-Shan], Xia, R.J.[Rong-Jie],
Rotation-based spatial-temporal feature learning from skeleton sequences for action recognition,
SIViP(14), No. 6, September 2020, pp. 1227-1234.
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Hu, G., Cui, B., Yu, S.,
Joint Learning in the Spatio-Temporal and Frequency Domains for Skeleton-Based Action Recognition,
MultMed(22), No. 9, September 2020, pp. 2207-2220.
IEEE DOI 2008
Frequency-domain analysis, Transforms, Frequency synchronization, Semantics, Training, Skeleton, Data mining, Action recognition, multi-task learning BibRef

Avola, D., Cascio, M., Cinque, L., Foresti, G.L., Massaroni, C., Rodolà, E.,
2-D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs,
MultMed(22), No. 10, October 2020, pp. 2481-2496.
IEEE DOI 2009
Skeleton, Feature extraction, Cameras, Recurrent neural networks, long short-term memory (LSTM) BibRef

Zhang, T., Zheng, W., Cui, Z., Zong, Y., Li, C., Zhou, X., Yang, J.,
Deep Manifold-to-Manifold Transforming Network for Skeleton-Based Action Recognition,
MultMed(22), No. 11, November 2020, pp. 2926-2937.
IEEE DOI 2010
Manifolds, Measurement, Covariance matrices, Feature extraction, Task analysis, Convolution, Kernel, Riemannian manifold, SPD matrix, action recognition BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Zhang, J.[Jing], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Action Recognition from Depth Maps Using Deep Convolutional Neural Networks,
HMS(46), No. 4, August 2016, pp. 498-509.
IEEE DOI 1608
data mining BibRef

Miao, S.Y.[Shuang-Yan], Hou, Y.H.[Yong-Hong], Gao, Z.M.[Zhi-Min], Xu, M.L.[Ming-Liang], Li, W.Q.[Wan-Qing],
A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition,
CirSysVideo(32), No. 7, July 2022, pp. 4893-4899.
IEEE DOI 2207
Convolution, Bones, Joints, Convolutional codes, Aggregates, Topology, Training, Graph convolutional network, action recognition, skeleton BibRef

Li, C.K.[Chuan-Kun], Li, S.[Shuai], Gao, Y.B.[Yan-Bo], Guo, L.[Lina], Li, W.Q.[Wan-Qing],
Improved Shift Graph Convolutional Network for Action Recognition With Skeleton,
SPLetters(30), 2023, pp. 438-442.
IEEE DOI 2305
Convolution, Skeleton, Computational complexity, Feature extraction, Convolutional neural networks, Kernel, skeleton BibRef

Hou, Y.H.[Yong-Hong], Li, Z.Y.[Zhao-Yang], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks,
CirSysVideo(28), No. 3, March 2018, pp. 807-811.
IEEE DOI 1804
convolution, feature extraction, feedforward neural nets, image coding, image colour analysis, image motion analysis, skeleton BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Depth Pooling Based Large-Scale 3-D Action Recognition with Convolutional Neural Networks,
MultMed(20), No. 5, May 2018, pp. 1051-1061.
IEEE DOI 1805
Dynamics, Feature extraction, Gesture recognition, Image recognition, Image segmentation, Motion segmentation, depth BibRef

Li, C.K.[Chuan-Kun], Hou, Y.H.[Yong-Hong], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Joint Distance Maps Based Action Recognition With Convolutional Neural Networks,
SPLetters(24), No. 5, May 2017, pp. 624-628.
IEEE DOI 1704
image colour analysis BibRef

Li, C.K.[Chuan-Kun], Hou, Y.H.[Yong-Hong], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Multiview-Based 3-D Action Recognition Using Deep Networks,
HMS(49), No. 1, February 2019, pp. 95-104.
IEEE DOI 1901
Skeleton, Trajectory, Feature extraction, Recurrent neural networks, Image color analysis, Encoding, three dimensional (3-D) BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Zhang, Y.Y.[Yu-Yao], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks,
CVPR17(416-425)
IEEE DOI 1711
Cameras, Feature extraction, Kernel, Optical imaging, Transforms, Videos BibRef

Zhang, J.[Jing], Li, W.Q.[Wan-Qing], Wang, P.C.[Pi-Chao], Ogunbona, P.[Philip], Liu, S.[Song], Tang, C.[Chang],
A Large Scale RGB-D Dataset for Action Recognition,
UHA3DS16(101-114).
Springer DOI 1806
BibRef

Zhang, H.Y.[Hao-Yuan], Hou, Y.H.[Yong-Hong], Wang, P.C.[Pi-Chao], Guo, Z.H.[Zi-Hui], Li, W.Q.[Wan-Qing],
SAR-NAS: Skeleton-based action recognition via neural architecture searching,
JVCIR(73), 2020, pp. 102942.
Elsevier DOI 2012
Neural architecture search, Action recognition, Skeleton
See also Large-Scale Continuous Gesture Recognition Using Convolutional Neural Networks. BibRef

Huang, H.[Hong'en], Su, H.[Hang], Chang, Z.G.[Zhi-Gang], Yu, M.Y.[Ming-Yang], Gao, J.L.[Jia-Lin], Li, X.Z.[Xin-Zhe], Zheng, S.B.[Shi-Bao],
Convolutional neural network with adaptive inferential framework for skeleton-based action recognition,
JVCIR(73), 2020, pp. 102925.
Elsevier DOI 2012
Skeleton-based action recognition, Pseudo image, Adaptive inferential framework, Different prior information BibRef

Gao, J.L.[Jia-Lin], He, T.[Tong], Zhou, X.[Xi], Ge, S.M.[Shi-Ming],
Skeleton-Based Action Recognition With Focusing-Diffusion Graph Convolutional Networks,
SPLetters(28), 2021, pp. 2058-2062.
IEEE DOI 2111
Focusing, Convolution, Skeleton, Transformers, Hidden Markov models, Context modeling, Aggregates, Focusing and diffusion, action recognition BibRef

Liu, K., Gao, L., Khan, N.M., Qi, L., Guan, L.,
A Multi-Stream Graph Convolutional Networks-Hidden Conditional Random Field Model for Skeleton-Based Action Recognition,
MultMed(23), 2021, pp. 64-76.
IEEE DOI 2012
Feature extraction, Convolution, Adaptation models, Neural networks, Bones, Message passing, GCN, CRF, skeleton, action recognition BibRef

Lee, I., Kim, D., Lee, S.,
3-D Human Behavior Understanding Using Generalized TS-LSTM Networks,
MultMed(23), 2021, pp. 415-428.
IEEE DOI 2012
Skeleton, Hidden Markov models, Feature extraction, Dynamics, Machine learning, Solid modeling, Human action recognition, temporal sequence analysis BibRef

Shao, Z., Li, Y., Zhang, H.,
Learning Representations From Skeletal Self-Similarities for Cross-View Action Recognition,
CirSysVideo(31), No. 1, January 2021, pp. 160-174.
IEEE DOI 2101
Skeleton, Feature extraction, Learning systems, Wrapping, Spatiotemporal phenomena, view-invariant representation BibRef

Chen, L., Lu, J., Song, Z., Zhou, J.,
Recurrent Semantic Preserving Generation for Action Prediction,
CirSysVideo(31), No. 1, January 2021, pp. 231-245.
IEEE DOI 2101
Semantics, Machine learning, Skeleton, Predictive models, Feature extraction, skeleton based action BibRef

Bian, C., Feng, W., Wan, L., Wang, S.,
Structural Knowledge Distillation for Efficient Skeleton-Based Action Recognition,
IP(30), 2021, pp. 2963-2976.
IEEE DOI 2102
Skeleton, Training, Pose estimation, Feature extraction, Videos, Joints, Knowledge engineering, Skeleton-based action recognition, gradient revision BibRef

Peng, W.[Wei], Shi, J.G.[Jin-Gang], Zhao, G.Y.[Guo-Ying],
Spatial Temporal Graph Deconvolutional Network for Skeleton-Based Human Action Recognition,
SPLetters(28), 2021, pp. 244-248.
IEEE DOI 2102
Deconvolution, Convolution, Kernel, Skeleton, Task analysis, Covariance matrices, Correlation, Graph neural network, over-smoothing BibRef

Peng, W.[Wei], Hong, X.P.[Xiao-Peng], Zhao, G.Y.[Guo-Ying],
Tripool: Graph triplet pooling for 3D skeleton-based action recognition,
PR(115), 2021, pp. 107921.
Elsevier DOI 2104
3D skeletal action recognition, ST-GCN, Graph pooling, Graph topology analysis BibRef

Hao, X.K.[Xiao-Ke], Li, J.[Jie], Guo, Y.C.[Ying-Chun], Jiang, T.[Tao], Yu, M.[Ming],
Hypergraph Neural Network for Skeleton-Based Action Recognition,
IP(30), 2021, pp. 2263-2275.
IEEE DOI 2102
convolutional neural nets, feature extraction, Fourier analysis, graph theory, image fusion, geometric relations BibRef

Yang, J., Liu, W., Yuan, J., Mei, T.,
Hierarchical Soft Quantization for Skeleton-Based Human Action Recognition,
MultMed(23), 2021, pp. 883-898.
IEEE DOI 2103
Skeleton, Feature extraction, Quantization (signal), Color, congenerous feature BibRef

Aouaidjia, K., Sheng, B., Li, P., Kim, J., Feng, D.D.,
Efficient Body Motion Quantification and Similarity Evaluation Using 3-D Joints Skeleton Coordinates,
SMCS(51), No. 5, May 2021, pp. 2774-2788.
IEEE DOI 2104
Skeleton, Measurement, Cameras, Sensors, Biological system modeling, Solid modeling, Pose estimation, Human-computer interaction, three-dimensional (3-D) human motion representation BibRef

Sun, N.[Ning], Leng, L.[Ling], Liu, J.X.[Ji-Xin], Han, G.[Guang],
Multi-stream slowFast graph convolutional networks for skeleton-based action recognition,
IVC(109), 2021, pp. 104141.
Elsevier DOI 2105
Action recognition, Graph convolutional network, Human skeleton, SlowFast network, Attention BibRef

Plizzari, C.[Chiara], Cannici, M.[Marco], Matteucci, M.[Matteo],
Skeleton-based action recognition via spatial and temporal transformer networks,
CVIU(208-209), 2021, pp. 103219.
Elsevier DOI 2106
BibRef
Earlier:
Spatial Temporal Transformer Network for Skeleton-based Action Recognition,
FBE20(694-701).
Springer DOI 2103
Representation learning, Graph CNN, Self-attention, 3D skeleton, Action recognition BibRef

Banerjee, A.[Avinandan], Singh, P.K.[Pawan Kumar], Sarkar, R.[Ram],
Fuzzy Integral-Based CNN Classifier Fusion for 3D Skeleton Action Recognition,
CirSysVideo(31), No. 6, June 2021, pp. 2206-2216.
IEEE DOI 2106
Skeleton, Feature extraction, Kinematics, Data mining, Image coding, convolutional neural network BibRef

Liu, X.L.[Xiao-Li], Yin, J.Q.[Jian-Qin], Liu, J.[Jin], Ding, P.X.[Peng-Xiang], Liu, J.[Jun], Liu, H.P.[Hua-Ping],
TrajectoryCNN: A New Spatio-Temporal Feature Learning Network for Human Motion Prediction,
CirSysVideo(31), No. 6, June 2021, pp. 2133-2146.
IEEE DOI 2106
Dynamics, Trajectory, Predictive models, Correlation, Biological system modeling, Robots, Benchmark testing, skeleton BibRef

Ding, P.X.[Peng-Xiang], Yin, J.Q.[Jian-Qin],
Towards More Realistic Human Motion Prediction With Attention to Motion Coordination,
CirSysVideo(32), No. 9, September 2022, pp. 5846-5858.
IEEE DOI 2209
Dynamics, Feature extraction, Predictive models, Adaptation models, Data mining, Skeleton, Convolution, Human motion prediction, enriched dynamics BibRef

Gupta, P.[Pranay], Thatipelli, A.[Anirudh], Aggarwal, A.[Aditya], Maheshwari, S.[Shubh], Trivedi, N.[Neel], Das, S.[Sourav], Sarvadevabhatla, R.K.[Ravi Kiran],
Quo Vadis, Skeleton Action Recognition?,
IJCV(129), No. 7, July 2021, pp. 2097-2112.
Springer DOI 2106
BibRef

Li, X.M.[Xing-Ming], Zhai, W.[Wei], Cao, Y.[Yang],
A tri-attention enhanced graph convolutional network for skeleton-based action recognition,
IET-CV(15), No. 2, 2021, pp. 110-121.
DOI Link 2106
BibRef

Yu, B.X.B.[Bruce X.B.], Liu, Y.[Yan], Chan, K.C.C.[Keith C.C.], Yang, Q.[Qintai], Wang, X.Y.[Xiao-Ying],
Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer's progression,
PR(119), 2021, pp. 108095.
Elsevier DOI 2108
Human action evaluation, Alzheimer's disease, Graph neural network, Abnormality detection BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Zhao, Y.H.[Yang-Heng], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng], Tian, Q.[Qi],
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction,
IP(30), 2021, pp. 7760-7775.
IEEE DOI 2109
BibRef
Earlier:
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction,
CVPR20(211-220)
IEEE DOI 2008
Feature extraction, Decoding, Predictive models, Convolution, Dynamics, Computational modeling, graph convolution. Dynamics, Convolution, Neural networks, Adaptation models BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Zhang, Z.J.[Zi-Jing], Xie, L.X.[Ling-Xi], Tian, Q.[Qi], Zhang, Y.[Ya],
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction,
ECCV22(VI:18-36).
Springer DOI 2211
BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Chen, X.[Xu], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng], Tian, Q.[Qi],
Symbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction,
PAMI(44), No. 6, June 2022, pp. 3316-3333.
IEEE DOI 2205
Feature extraction, Magnetic heads, Joints, Convolution, Task analysis, Symbiosis, graph inference BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Liu, Z.H.[Zi-Hui], Zhang, Z.J.[Zi-Jing], Xie, L.X.[Ling-Xi], Tian, Q.[Qi], Zhang, Y.[Ya],
Skeleton Graph Scattering Networks for 3D Skeleton-based Human Motion Prediction,
GSP-CV21(854-864)
IEEE DOI 2112
Convolution, Aggregates, Scattering, Feature extraction BibRef

Feng, H.[Hui], Wang, S.S.[Shan-Shan], Xu, H.X.[Hai-Xiang], Ge, S.S.[Shuzhi Sam],
Object Activity Scene Description, Construction, and Recognition,
Cyber(51), No. 10, October 2021, pp. 5082-5092.
IEEE DOI 2110
Skeleton, Feature extraction, Hip, Cybernetics, Trajectory, Data mining, Histograms, Convolutional neural network (CNN), scene recognition BibRef

Yang, H.[Hao], Yan, D.[Dan], Zhang, L.[Li], Sun, Y.[Yunda], Li, D.[Dong], Maybank, S.J.[Stephen J.],
Feedback Graph Convolutional Network for Skeleton-Based Action Recognition,
IP(31), 2022, pp. 164-175.
IEEE DOI 2112
Skeleton, Feature extraction, Joints, Semantics, Predictive models, Data models, Convolution, Feedback mechanism, action recognition BibRef

Naveenkumar, M., Domnic, S.,
Spatio Temporal Joint Distance Maps for Skeleton-Based Action Recognition Using Convolutional Neural Networks,
IJIG(21), No. 5 2021, pp. 2140001.
DOI Link 2201
BibRef

Koniusz, P.[Piotr], Wang, L.[Lei], Cherian, A.[Anoop],
Tensor Representations for Action Recognition,
PAMI(44), No. 2, February 2022, pp. 648-665.
IEEE DOI 2201
Tensors, Kernel, Skeleton, Correlation, Optical imaging, Higher order statistics, CNN, 3D skeletons, power normalization BibRef

Koniusz, P.[Piotr], Cherian, A.[Anoop], Porikli, F.M.[Fatih M.],
Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons,
ECCV16(IV: 37-53).
Springer DOI 1611
BibRef
Earlier: A1, A2, Only:
Sparse Coding for Third-Order Super-Symmetric Tensor Descriptors with Application to Texture Recognition,
CVPR16(5395-5403)
IEEE DOI 1612
BibRef

Shen, X.P.[Xiang-Pei], Ding, Y.R.[Yan-Rui],
Human skeleton representation for 3D action recognition based on complex network coding and LSTM,
JVCIR(82), 2022, pp. 103386.
Elsevier DOI 2201
Skeleton-based action recognition, Complex network coding, LSTM, Feature extraction BibRef

Zang, Y.[Ying], Yang, D.S.[Dong-Sheng], Liu, T.J.[Tian-Jiao], Li, H.[Hui], Zhao, S.G.[Shu-Guang], Liu, Q.S.[Qing-Shan],
SparseShift-GCN: High precision skeleton-based action recognition,
PRL(153), 2022, pp. 136-143.
Elsevier DOI 2201
BibRef

Alsarhan, T.[Tamam], Ali, U.[Usman], Lu, H.T.[Hong-Tao],
Enhanced discriminative graph convolutional network with adaptive temporal modelling for skeleton-based action recognition,
CVIU(216), 2022, pp. 103348.
Elsevier DOI 2202
Skeleton-based action recognition, Graph convolutional network, Squeeze and excitation, Adaptive temporal modelling BibRef

Kong, J.[Jun], Bian, Y.H.[Yu-Hang], Jiang, M.[Min],
MTT: Multi-Scale Temporal Transformer for Skeleton-Based Action Recognition,
SPLetters(29), 2022, pp. 528-532.
IEEE DOI 2202
Feature extraction, Transformers, Kernel, Skeleton, Data mining, Task analysis, Convolution, Skeleton-based action recognition, multi-scale temporal embedding BibRef

Yu, L.[Lubin], Tian, L.[Lianfang], Du, Q.[Qiliang], Bhutto, J.A.[Jameel Ahmed],
Multi-stream adaptive spatial-temporal attention graph convolutional network for skeleton-based action recognition,
IET-CV(16), No. 2, 2022, pp. 143-158.
DOI Link 2202
computer graphics, convolutional neural nets, graphics processing units, space-time adaptive processing BibRef

Song, S.[Sijie], Liu, J.Y.[Jia-Ying], Lin, L.[Lilang], Guo, Z.M.[Zong-Ming],
Learning to Recognize Human Actions From Noisy Skeleton Data Via Noise Adaptation,
MultMed(24), 2022, pp. 1152-1163.
IEEE DOI 2203
Skeleton, Noise measurement, Adaptation models, Feature extraction, Cameras, Pose estimation, Action recognition, noisy skeletons, noise adaptation BibRef

Tang, J.[Jun], Wang, Y.J.[Yan-Jiang], Fu, S.C.[Si-Chao], Liu, B.[Baodi], Liu, W.F.[Wei-Feng],
A graph convolutional neural network model with Fisher vector encoding and channel-wise spatial-temporal aggregation for skeleton-based action recognition,
IET-IPR(16), No. 5, 2022, pp. 1433-1443.
DOI Link 2203
BibRef

Liu, M.Y.[Meng-Yuan], Bao, Y.[Youneng], Liang, Y.S.[Yong-Sheng], Meng, F.[Fanyang],
Spatial-Temporal Asynchronous Normalization for Unsupervised 3D Action Representation Learning,
SPLetters(29), 2022, pp. 632-636.
IEEE DOI 2203
Skeleton, Shape, Decoding, Task analysis, Representation learning, Data mining, 3D action, representation learning BibRef

Xie, Y.L.[Yu-Lai], Zhang, Y.[Yang], Ren, F.[Fang],
Temporal-Enhanced Graph Convolution Network for Skeleton-Based Action Recognition,
IET-CV(16), No. 3, 2022, pp. 266-279.
DOI Link 2204
causal convolution, graph convolution network, long-range temporal correlation, temporal sequence modelling BibRef

Ng, W.[Wing], Zhang, M.Y.[Ming-Yang], Wang, T.[Ting],
Multi-Localized Sensitive Autoencoder-Attention-LSTM For Skeleton-based Action Recognition,
MultMed(24), 2022, pp. 1678-1690.
IEEE DOI 2204
Skeleton, Feature extraction, Joints, Hidden Markov models, Convolution, Task analysis, Bones, Localized Stochastic Sensitive Autoencoder (LiSSA) BibRef

Wu, C.[Cong], Wu, X.J.[Xiao-Jun], Kittler, J.V.[Josef V.],
Graph2Net: Perceptually-Enriched Graph Learning for Skeleton-Based Action Recognition,
CirSysVideo(32), No. 4, April 2022, pp. 2120-2132.
IEEE DOI 2204
BibRef
Earlier:
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition,
SGRL19(1740-1748)
IEEE DOI 2004
Skeleton, Hidden Markov models, Feature extraction, Spatiotemporal phenomena, Convolution, Technological innovation, graph learning. convolutional neural nets, feature extraction, graph theory, image fusion, image representation, spatiotemporal phenomena, Skeleton Based Action Recognition BibRef

Shu, X.B.[Xiang-Bo], Zhang, L.Y.[Li-Yan], Qi, G.J.[Guo-Jun], Liu, W.[Wei], Tang, J.H.[Jin-Hui],
Spatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction,
PAMI(44), No. 6, June 2022, pp. 3300-3315.
IEEE DOI 2205
Skeleton, Predictive models, Spatiotemporal phenomena, Solid modeling, Recurrent neural networks, Spatial coherence, recurrent neural network BibRef

Wang, H.R.[Hao-Ran], Yu, B.S.[Bao-Sheng], Li, J.Q.[Jia-Qi], Zhang, L.L.[Lin-Lin], Chen, D.Y.[Dong-Yue],
Multi-Stream Interaction Networks for Human Action Recognition,
CirSysVideo(32), No. 5, May 2022, pp. 3050-3060.
IEEE DOI 2205
Skeleton, Proposals, Footwear, Deep learning, Image recognition, Fuses, Adaptation models, Temporal HOI analysis, human action recognition BibRef

Zheng, H.[Hui], Zhang, X.M.[Xin-Ming],
A Cross View Learning Approach for Skeleton-Based Action Recognition,
CirSysVideo(32), No. 5, May 2022, pp. 3061-3072.
IEEE DOI 2205
Convolution, Joints, Task analysis, Feature extraction, Data models, Bones, Recurrent neural networks, HAR, fusion, inter-view, multi-scale, skeleton BibRef

Qin, X.F.[Xiao-Fei], Li, H.[Hao], Liu, Y.[Yuru], Yu, J.[Jiabin], He, C.X.[Chang-Xiang], Zhang, X.[Xuedian],
Multi-stage part-aware graph convolutional network for skeleton-based action recognition,
IET-IPR(16), No. 8, 2022, pp. 2063-2074.
DOI Link 2205
BibRef

Xia, R.J.[Rong-Jie], Li, Y.[Yanshan], Luo, W.H.[Wen-Han],
LAGA-Net: Local-and-Global Attention Network for Skeleton Based Action Recognition,
MultMed(24), 2022, pp. 2648-2661.
IEEE DOI 2205
Skeleton, Feature extraction, Joints, Adaptation models, Kernel, Computational modeling, Motion Enhancement BibRef

Xu, B.Q.[Bin-Qian], Shu, X.B.[Xiang-Bo], Song, Y.[Yan],
X-Invariant Contrastive Augmentation and Representation Learning for Semi-Supervised Skeleton-Based Action Recognition,
IP(31), 2022, pp. 3852-3867.
IEEE DOI 2206
Skeleton, Representation learning, Joints, Bones, Semisupervised learning, Recurrent neural networks, contrastive learning BibRef

Shu, X.B.[Xiang-Bo], Xu, B.Q.[Bin-Qian], Zhang, L.Y.[Li-Yan], Tang, J.H.[Jin-Hui],
Multi-Granularity Anchor-Contrastive Representation Learning for Semi-Supervised Skeleton-Based Action Recognition,
PAMI(45), No. 6, June 2023, pp. 7559-7576.
IEEE DOI 2305
Skeleton, Task analysis, Loss measurement, Joints, Semantics, Data models, Pattern recognition, Action recognition, skeleton, anchor graph BibRef

Sun, B.[Bin], Wang, S.[Shaofan], Kong, D.[Dehui], Wang, L.C.[Li-Chun], Yin, B.C.[Bao-Cai],
Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model,
Cyber(52), No. 6, June 2022, pp. 4837-4849.
IEEE DOI 2207
Feature extraction, Real-time systems, Computational modeling, Joints, Solid modeling, Kinematics, Analytical models, sparse representation BibRef

Zhou, J.X.[Jia-Xin], Komuro, T.[Takashi],
An asymmetrical-structure auto-encoder for unsupervised representation learning of skeleton sequences,
CVIU(222), 2022, pp. 103491.
Elsevier DOI 2209
BibRef
And:
Recognizing Fall Actions from Videos Using Reconstruction Error of Variational Autoencoder,
ICIP19(3372-3376)
IEEE DOI 1910
Action recognition, Unsupervised representation learning. Image sequence analysis, Video surveillance BibRef

Rao, H.C.[Hao-Cong], Wang, S.Q.[Si-Qi], Hu, X.P.[Xi-Ping], Tan, M.K.[Ming-Kui], Guo, Y.[Yi], Cheng, J.[Jun], Liu, X.W.[Xin-Wang], Hu, B.[Bin],
A Self-Supervised Gait Encoding Approach With Locality-Awareness for 3D Skeleton Based Person Re-Identification,
PAMI(44), No. 10, October 2022, pp. 6649-6666.
IEEE DOI 2209
Skeleton, Encoding, Task analysis, Computational modeling, Solid modeling, Feature extraction, contrastive learning BibRef

Liu, C.W.[Cui-Wei], Zhao, X.X.[Xiao-Xue], Li, Z.K.[Zhao-Kui], Yan, Z.[Zhuo], Du, C.[Chong],
A Novel Two-Stage Knowledge Distillation Framework for Skeleton-Based Action Prediction,
SPLetters(29), 2022, pp. 1918-1922.
IEEE DOI 2209
Adaptation models, Predictive models, Skeleton, Training, Probability distribution, Writing, Action prediction, skeletons BibRef

Liu, K.Y.[Kai-Yuan], Li, Y.H.[Yun-Heng], Xu, Y.F.[Yuan-Feng], Liu, S.[Shuai], Liu, S.L.[Sheng-Lan],
Spatial Focus Attention for Fine-Grained Skeleton-Based Action Tasks,
SPLetters(29), 2022, pp. 1883-1887.
IEEE DOI 2209
Task analysis, Topology, Skeleton, Sports, Semantics, Heuristic algorithms, Encoding, temporal action segmentation BibRef

Kim, B.[Boeun], Choi, J.Y.[Jin Young],
Learning spectral transform for 3D human motion prediction,
CVIU(223), 2022, pp. 103548.
Elsevier DOI 2210
Human motion prediction, 3D Human motion prediction, Skeleton-based human motion prediction, Spectral transform BibRef

Nguyen, T.T.[Tien-Thanh], Pham, D.T.[Dinh-Tan], Vu, H.[Hai], Le, T.L.[Thi-Lan],
A robust and efficient method for skeleton-based human action recognition and its application for cross-dataset evaluation,
IET-CV(16), No. 8, 2022, pp. 709-726.
DOI Link 2210
BibRef

Farnoosh, A.[Amirreza], Ostadabbas, S.[Sarah],
Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion,
IJCV(130), No. 11, November 2022, pp. 2695-2706.
Springer DOI 2210
BibRef

Ma, H.[Hao], Yang, Z.[Zaiyue], Liu, H.Y.[Hao-Yang],
Fine-Grained Unsupervised Temporal Action Segmentation and Distributed Representation for Skeleton-Based Human Motion Analysis,
Cyber(52), No. 12, December 2022, pp. 13411-13424.
IEEE DOI 2212
Motion segmentation, Hidden Markov models, Semantics, Analytical models, Motion analysis, Distributed representation, temporal action segmentation BibRef

Liu, Z.G.[Zhen-Guang], Wu, S.[Shuang], Jin, S.Y.[Shu-Yuan], Ji, S.[Shouling], Liu, Q.[Qi], Lu, S.J.[Shi-Jian], Cheng, L.[Li],
Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction,
PAMI(45), No. 1, January 2023, pp. 681-697.
IEEE DOI 2212
Context modeling, Mice, Joints, Predictive models, Kinematics, Task analysis, Motion prediction, motion context, pose representation BibRef

Wu, K.L.[Kun-Lun], Gong, X.[Xun],
Dynamic Channel-Aware Subgraph Interactive Networks for Skeleton-Based Action Recognition,
SPLetters(29), 2022, pp. 2592-2596.
IEEE DOI 2301
Skeleton, Topology, Solid modeling, Adaptation models, Convolution, Computational modeling, Collaboration, Graph neural network, skeleton-based action recognition BibRef

Xiong, X.[Xin], Min, W.D.[Wei-Dong], Wang, Q.[Qi], Zha, C.[Cheng],
Human Skeleton Feature Optimizer and Adaptive Structure Enhancement Graph Convolution Network for Action Recognition,
CirSysVideo(33), No. 1, January 2023, pp. 342-353.
IEEE DOI 2301
Feature extraction, Skeleton, Convolution, Data mining, Directed graphs, Smart cities, Kernel, Action recognition, adaptive pooling operation BibRef

Song, Y.F.[Yi-Fan], Zhang, Z.[Zhang], Shan, C.F.[Cai-Feng], Wang, L.[Liang],
Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition,
PAMI(45), No. 2, February 2023, pp. 1474-1488.
IEEE DOI 2301
Computational modeling, Skeleton, Feature extraction, Convolution, Task analysis, Compounds, Training, Action recognition, separable convolution BibRef

Wen, Y.H.[Yu-Hui], Gao, L.[Lin], Fu, H.B.[Hong-Bo], Zhang, F.L.[Fang-Lue], Xia, S.H.[Shi-Hong], Liu, Y.J.[Yong-Jin],
Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition,
PAMI(45), No. 2, February 2023, pp. 2009-2023.
IEEE DOI 2301
Skeleton, Feature extraction, Joints, Convolutional codes, Topology, Training, Sparse matrices, Action recognition, skeleton sequence BibRef

Cui, M.M.[Meng-Meng], Wang, W.[Wei], Zhang, K.[Kunbo], Sun, Z.A.[Zhen-An], Wang, L.[Liang],
Pose-Appearance Relational Modeling for Video Action Recognition,
IP(32), 2023, pp. 295-308.
IEEE DOI 2301
Visualization, Solid modeling, Skeleton, Feature extraction, Optical flow, Context modeling, Action recognition, temporal attention LSTM BibRef

Li, Z.H.[Zhi-Heng], Gong, X.Y.[Xu-Yuan], Song, R.[Ran], Duan, P.[Peng], Liu, J.[Jun], Zhang, W.[Wei],
SMAM: Self and Mutual Adaptive Matching for Skeleton-Based Few-Shot Action Recognition,
IP(32), 2023, pp. 392-402.
IEEE DOI 2301
Skeleton, Feature extraction, Training, Task analysis, Convolution, Topology, Semantics, Skeleton-based, action recognition, few-shot learning BibRef

Zhu, Y.S.[Yi-Sheng], Shuai, H.[Hui], Liu, G.C.[Guang-Can], Liu, Q.S.[Qing-Shan],
Multilevel Spatial-Temporal Excited Graph Network for Skeleton-Based Action Recognition,
IP(32), 2023, pp. 496-508.
IEEE DOI 2301
Skeleton, Convolution, Topology, Head, Feature extraction, Biological system modeling, Transformers, multilevel spatial-temporal modeling BibRef

Wu, L.[Liyu], Zhang, C.[Can], Zou, Y.X.[Yue-Xian],
SpatioTemporal focus for skeleton-based action recognition,
PR(136), 2023, pp. 109231.
Elsevier DOI 2301
Action recognition, Skeleton topology, Graph convolutional network BibRef

Gao, X.H.[Xue-Hao], Yang, Y.[Yang], Zhang, Y.M.[Yi-Meng], Li, M.[Maosen], Yu, J.G.[Jin-Gang], Du, S.[Shaoyi],
Efficient Spatio-Temporal Contrastive Learning for Skeleton-Based 3-D Action Recognition,
MultMed(25), 2023, pp. 405-417.
IEEE DOI 2302
Task analysis, Skeleton, Encoding, Training, Feature extraction, Visualization, self-supervised method, observation scene, 3D action recognition BibRef

Weng, L.[Libo], Lou, W.D.[Wei-Dong], Shen, X.[Xin], Gao, F.[Fei],
A 3D Graph Convolutional Networks Model for 2D Skeleton-Based Human Action Recognition,
IET-IPR(17), No. 3, 2023, pp. 773-783.
DOI Link 2303
2D human action recognition, 3D convolutional neural networks, attention mechanism, graph convolutional neural networks, skeleton sequences BibRef

Bian, C.L.[Cun-Ling], Feng, W.[Wei], Meng, F.[Fanbo], Wang, S.[Song],
Global-local contrastive multiview representation learning for skeleton-based action recognition,
CVIU(229), 2023, pp. 103655.
Elsevier DOI 2303
Skeleton-based action recognition, Contrastive representation learning, Multiview, Graph convolutional network BibRef

Khezerlou, F., Baradarani, A., Balafar, M.A.,
A convolutional autoencoder model with weighted multi-scale attention modules for 3D skeleton-based action recognition,
JVCIR(92), 2023, pp. 103781.
Elsevier DOI 2303
Human action recognition, Motion trajectories, 3DPo-CDP descriptor, Change direction patterns, Pose features, WMS block BibRef

Huang, Z.X.[Zeng-Xi], Qin, Y.S.[Yu-Song], Lin, X.B.[Xia-Bing], Liu, T.L.[Tian-Lin], Feng, Z.H.[Zhen-Hua], Liu, Y.G.[Yi-Guang],
Motion-Driven Spatial and Temporal Adaptive High-Resolution Graph Convolutional Networks for Skeleton-Based Action Recognition,
CirSysVideo(33), No. 4, April 2023, pp. 1868-1883.
IEEE DOI 2304
Skeleton, Feature extraction, Convolution, Adaptation models, Joints, Data mining, Correlation, Graph convolutional networks, high-resolution graph BibRef

Gedamu, K.[Kumie], Ji, Y.L.[Yan-Li], Gao, L.[LingLing], Yang, Y.[Yang], Shen, H.T.[Heng Tao],
Relation-mining self-attention network for skeleton-based human action recognition,
PR(139), 2023, pp. 109455.
Elsevier DOI 2304
Action recognition, Relation-mining self-attention, Pairwise self-attention, Unary self-attention, Position attention BibRef

Nikpour, B.[Bahareh], Armanfard, N.[Narges],
Spatio-temporal hard attention learning for skeleton-based activity recognition,
PR(139), 2023, pp. 109428.
Elsevier DOI 2304
Temporal attention, Spatial attention, Spatio-temporal attention, Activity recognition, Deep reinforcement learning BibRef

Wang, W.Q.[Wen-Qian], Chang, F.[Faliang], Liu, C.S.[Chun-Sheng], Li, G.X.[Guang-Xin], Wang, B.[Bin],
GA-Net: A Guidance Aware Network for Skeleton-Based Early Activity Recognition,
MultMed(25), 2023, pp. 1061-1073.
IEEE DOI 2305
Measurement, Skeleton, Dams, Feature extraction, Task analysis, Spatiotemporal phenomena, metric learning BibRef

Dai, M.[Meng], Sun, Z.H.[Zhong-Hua], Wang, T.Y.[Tian-Yi], Feng, J.C.[Jin-Chao], Jia, K.[Kebin],
Global spatio-temporal synergistic topology learning for skeleton-based action recognition,
PR(140), 2023, pp. 109540.
Elsevier DOI 2305
Action recognition, Spatio-temporal synergistic, Skeleton, Topology learning BibRef

Hedegaard, L.[Lukas], Heidari, N.[Negar], Iosifidis, A.[Alexandros],
Continual spatio-temporal graph convolutional networks,
PR(140), 2023, pp. 109528.
Elsevier DOI 2305
Graph convolutional networks, Continual inference, Efficient deep learning, Skeleton-based action recognition BibRef

Wang, M.[Minsi], Ni, B.B.[Bing-Bing], Yang, X.K.[Xiao-Kang],
Learning Multi-View Interactional Skeleton Graph for Action Recognition,
PAMI(45), No. 6, June 2023, pp. 6940-6954.
IEEE DOI 2305
Skeleton, Topology, Feature extraction, Convolution, Network topology, Recurrent neural networks, Action recognition, hierarchical method BibRef

Xu, L.[Leiyang], Wang, Q.[Qiang], Lin, X.T.[Xiao-Tian], Yuan, L.[Lin],
An efficient framework for few-shot skeleton-based temporal action segmentation,
CVIU(232), 2023, pp. 103707.
Elsevier DOI 2305
Temporal action segmentation, Data segmentation, Synthetic action sequences, Connectionist temporal classification BibRef

Liu, W.X.[Wen-Xuan], Zhong, X.[Xian], Zhou, Z.[Zhuo], Jiang, K.[Kui], Wang, Z.[Zheng], Lin, C.W.[Chia-Wen],
Dual-Recommendation Disentanglement Network for View Fuzz in Action Recognition,
IP(32), 2023, pp. 2719-2733.
IEEE DOI 2305
Feature extraction, Optical flow, Visualization, Computer science, Skeleton, Predictive models, Training, Action recognition, view fuzz, mutual learning BibRef

Peng, K.[Kunyu], Roitberg, A.[Alina], Yang, K.[Kailun], Zhang, J.M.[Jia-Ming], Stiefelhagen, R.[Rainer],
Delving Deep Into One-Shot Skeleton-Based Action Recognition With Diverse Occlusions,
MultMed(25), 2023, pp. 1489-1504.
IEEE DOI 2305
Transformers, Task analysis, Benchmark testing, Joints, Prototypes, Image recognition, human activity recognition, representation learning BibRef

Zeng, Q.[Qinyang], Liu, C.[Chengju], Liu, M.[Ming], Chen, Q.J.[Qi-Jun],
Contrastive 3D Human Skeleton Action Representation Learning via CrossMoCo With Spatiotemporal Occlusion Mask Data Augmentation,
MultMed(25), 2023, pp. 1564-1574.
IEEE DOI 2305
Skeleton, Feature extraction, Spatiotemporal phenomena, Data mining, Joints, Learning systems, Cross contrastive learning, human skeleton action recognition BibRef

Shen, J.X.[Jun-Xiao], Dudley, J.[John], Kristensson, P.O.[Per Ola],
The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition,
FG21(1-8)
IEEE DOI 2303
Deep learning, Training data, Stochastic processes, Inspection, Generative adversarial networks, Data models, Spatiotemporal phenomena BibRef

Bandi, C.[Chaitanya], Thomas, U.[Ulrike],
Skeleton-based Action Recognition for Human-Robot Interaction using Self-Attention Mechanism,
FG21(1-8)
IEEE DOI 2303
Recurrent neural networks, Pipelines, Human-robot interaction, Predictive models, Encoding, Skeleton, Real-time systems BibRef

Chen, T.[Tailin], Zhou, D.[Desen], Wang, J.[Jian], Wang, S.D.[Shi-Dong], He, Q.[Qian], Hu, C.Y.[Chuan-Yang], Ding, E.[Errui], Guan, Y.[Yu], He, X.M.[Xu-Ming],
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition,
FG23(1-8)
IEEE DOI 2303
Visualization, Fuses, Face recognition, Prototypes, Gesture recognition, Benchmark testing, Skeleton BibRef

Zhu, A.[Anqi], Ke, Q.H.[Qiu-Hong], Gong, M.M.[Ming-Ming], Bailey, J.[James],
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition,
WACV23(6027-6036)
IEEE DOI 2302
Training, Adaptation models, Visualization, Adaptive systems, Measurement units, Face recognition BibRef

Xu, S.H.[Shi-Hao], Rao, H.[Haocong], Hu, X.[Xiping], Cheng, J.[Jun], Hu, B.[Bin],
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition,
MultMed(25), 2023, pp. 624-634.
IEEE DOI 2302
Skeleton, Prototypes, Encoding, Task analysis, Semantics, Decoding, Prediction algorithms, Prototypical contrast, skeleton based action recognition BibRef

Kang, M.S.[Min-Seok], Kang, D.[Dongoh], Kim, H.S.[Han-Saem],
Efficient Skeleton-Based Action Recognition via Joint-Mapping strategies,
WACV23(3392-3401)
IEEE DOI 2302
Visualization, Embedded systems, Computational modeling, Surveillance, Pose estimation, Real-time systems, Robotics BibRef

Shang, M.Z.[Ming-Zhou], Huang, Q.[Qian], Wang, Y.M.[Yi-Ming], Bian, X.[Xiang], Jiang, C.X.[Chuan-Xu], Liu, J.W.[Ji-Wen],
Skeleton-Based Dumbbell Fitness Action Recognition Using Two-Stream LSTM Network,
ICIVC22(31-36)
IEEE DOI 2301
Technological innovation, Image recognition, Clustering algorithms, Feature extraction, Skeleton, LSTM BibRef

Liu, C.[Cuiwei], Zhao, X.X.[Xiao-Xue], Yan, Z.[Zhuo], Jiang, Y.Z.[You-Zhi], Shi, X.B.[Xiang-Bin],
A Graph Convolutional Network with Early Attention Module for Skeleton-based Action Prediction,
ICPR22(1266-1272)
IEEE DOI 2212
Convolution, Feature extraction, Skeleton, Character recognition, Task analysis BibRef

Qin, H.[Hushan], Cheng, J.[Jun], Song, C.Q.[Cheng-Qun], Hao, F.[Fusheng], Cheng, Q.[Qin],
Structure-Preserving View-Invariant Skeleton Representation for Action Detection,
ICPR22(3190-3196)
IEEE DOI 2212
Representation learning, Limiting, Stacking, Discrete Fourier transforms, Feature extraction, Skeleton, skeleton representation BibRef

Xing, H.[Hao], Burschka, D.[Darius],
Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network,
ICPR22(3333-3340)
IEEE DOI 2212
Adaptation models, Head, Skeleton, Natural language processing, Graph neural networks, Planning, Kinetic theory BibRef

Tang, Z.H.[Zhi-Hao], Xia, H.L.[Hai-Lun], Gao, X.K.[Xin-Kai], Gao, F.[Feng], Feng, C.Y.[Chun-Yan],
Skeleton-Based Action Recognition with Graph Involution Network,
ICPR22(3348-3354)
IEEE DOI 2212
Spirals, Convolution, Network topology, Benchmark testing, Logic gates, Skeleton, Topology BibRef

Kim, J.[Jaehwan], Lee, J.[Junsuk],
Global Positional Self-Attention for Skeleton-Based Action Recognition,
ICPR22(3355-3361)
IEEE DOI 2212
Visualization, Correlation, Semantics, Gesture recognition, Benchmark testing, Encoding BibRef

Zhu, H.D.[Hai-Dong], Zheng, Z.H.[Zhao-Heng], Nevatia, R.[Ram],
Temporal Shift and Attention Modules for Graphical Skeleton Action Recognition,
ICPR22(3145-3151)
IEEE DOI 2212
Convolution, Shape, Biological system modeling, Skeleton, Kinetic theory, Videos BibRef

Golda, T.[Thomas], Thiemich, J.[Johanna], Cormier, M.[Mickael], Beyerer, J.[Jürgen],
For the Sake of Privacy: Skeleton-Based Salient Behavior Recognition,
ICIP22(3983-3987)
IEEE DOI 2211
Data privacy, Image recognition, Video surveillance, Behavioral sciences, Safety, Task analysis, Anomaly detection, privacy friendly BibRef

Hao, Y.L.[Yan-Ling], Shi, Z.Y.[Zhi-Yuan], Liu, Y.[Yuanwei],
WiFi-Based Spatiotemporal Human Action Perception,
ICIP22(3581-3585)
IEEE DOI 2211
Support vector machines, Visualization, Neural networks, Line-of-sight propagation, Benchmark testing, Skeleton, wireless-vision BibRef

Kilis, N.[Nikolaos], Papaioannidis, C.[Christos], Mademlis, I.[Ioannis], Pitas, I.[Ioannis],
An Efficient Framework for Human Action Recognition Based on Graph Convolutional Networks,
ICIP22(1441-1445)
IEEE DOI 2211
Image recognition, Convolution, Architecture, Pipelines, Skeleton, Skeleton-based human action recognition, feature imputation BibRef

Kim, B.[Boeun], Chang, H.J.[Hyung Jin], Kim, J.[Jungho], Choi, J.Y.[Jin Young],
Global-Local Motion Transformer for Unsupervised Skeleton-Based Action Learning,
ECCV22(IV:209-225).
Springer DOI 2211
BibRef

Ma, N.[Ning], Zhang, H.Y.[Hong-Yi], Li, X.[Xuhui], Zhou, S.[Sheng], Zhang, Z.[Zhen], Wen, J.[Jun], Li, H.F.[Hai-Feng], Gu, J.[Jingjun], Bu, J.J.[Jia-Jun],
Learning Spatial-Preserved Skeleton Representations for Few-Shot Action Recognition,
ECCV22(IV:174-191).
Springer DOI 2211
BibRef

Pang, Y.S.[Yun-Sheng], Ke, Q.H.[Qiu-Hong], Rahmani, H.[Hossein], Bailey, J.[James], Liu, J.[Jun],
IGFormer: Interaction Graph Transformer for Skeleton-Based Human Interaction Recognition,
ECCV22(XXV:605-622).
Springer DOI 2211
BibRef

Kwon, T.[Taein], Tekin, B.[Bugra], Tang, S.[Siyu], Pollefeys, M.[Marc],
Context-Aware Sequence Alignment using 4D Skeletal Augmentation,
CVPR22(8162-8172)
IEEE DOI 2210
Training, Pose estimation, Self-supervised learning, Transforms, Transformers, Behavior analysis, Self- semi- meta- Video analysis and understanding BibRef

Chi, H.G.[Hyung-Gun], Ha, M.H.[Myoung Hoon], Chi, S.G.[Seung-Geun], Lee, S.W.[Sang Wan], Huang, Q.X.[Qi-Xing], Ramani, K.[Karthik],
InfoGCN: Representation Learning for Human Skeleton-based Action Recognition,
CVPR22(20154-20164)
IEEE DOI 2210
Representation learning, Convolution, Design methodology, Benchmark testing, Skeleton, Encoding, Representation learning BibRef

Duan, H.D.[Hao-Dong], Zhao, Y.[Yue], Chen, K.[Kai], Lin, D.[Dahua], Dai, B.[Bo],
Revisiting Skeleton-based Action Recognition,
CVPR22(2959-2968)
IEEE DOI 2210
Heating systems, Scalability, Benchmark testing, Feature extraction, Skeleton, Robustness, Action and event recognition BibRef

Salzmann, T.[Tim], Pavone, M.[Marco], Ryll, M.[Markus],
Motron: Multimodal Probabilistic Human Motion Forecasting,
CVPR22(6447-6456)
IEEE DOI 2210
Uncertainty, Computational modeling, Predictive models, Probabilistic logic, Prediction algorithms, Skeleton, Planning, Statistical methods BibRef

Moliner, O.[Olivier], Huang, S.X.[Sang-Xia], Åström, K.[Kalle],
Bootstrapped Representation Learning for Skeleton-Based Action Recognition,
L3D-IVU22(4153-4163)
IEEE DOI 2210
Representation learning, Transfer learning, Pipelines, Self-supervised learning, Cameras, Sampling methods BibRef

Liu, Y.[Yan], Deng, Y.L.[Yue-Lin], Su, J.P.[Jin-Ping], Wang, R.N.[Ruo-Nan], Li, C.[Chi],
Multiple Input Branches Shift Graph Convolutional Network with DropEdge for Skeleton-Based Action Recognition,
CIAP22(I:584-596).
Springer DOI 2205
BibRef

Yang, S.Y.[Si-Yuan], Liu, J.[Jun], Lu, S.J.[Shi-Jian], Er, M.H.[Meng Hwa], Kot, A.C.[Alex C.],
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning,
ICCV21(13403-13413)
IEEE DOI 2203
Representation learning, Point cloud compression, Image color analysis, Supervised learning, Stacking, BibRef

Li, T.J.[Tian-Jiao], Ke, Q.H.[Qiu-Hong], Rahmani, H.[Hossein], Ho, R.E.[Rui En], Ding, H.H.[Heng-Hui], Liu, J.[Jun],
Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data,
ICCV21(13414-13423)
IEEE DOI 2203
Semantics, Skeleton, Task analysis, Action and behavior recognition, Gestures and body pose BibRef

Shi, L.[Lei], Zhang, Y.F.[Yi-Fan], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition,
ICCV21(13393-13402)
IEEE DOI 2203
Adaptation models, Computational modeling, Process control, Manuals, Skeleton, Data models, Action and behavior recognition, Video analysis and understanding BibRef

Villegas, R.[Ruben], Ceylan, D.[Duygu], Hertzmann, A.[Aaron], Yang, J.[Jimei], Saito, J.[Jun],
Contact-Aware Retargeting of Skinned Motion,
ICCV21(9700-9709)
IEEE DOI 2203
Torso, Geometry, Recurrent neural networks, Shape, Motion estimation, Skeleton, Encoding, Motion and tracking, Gestures and body pose BibRef

Friji, R.[Rasha], Drira, H.[Hassen], Chaieb, F.[Faten], Kchok, H.[Hamza], Kurtek, S.[Sebastian],
Geometric Deep Neural Network using Rigid and Non-Rigid Transformations for Human Action Recognition,
ICCV21(12591-12600)
IEEE DOI 2203
Deep learning, Shape, Neural networks, Skeleton, Action and behavior recognition, Motion and tracking, Representation learning BibRef

Su, Y.K.[Yu-Kun], Lin, G.S.[Guo-Sheng], Wu, Q.Y.[Qing-Yao],
Self-supervised 3D Skeleton Action Representation Learning with Motion Consistency and Continuity,
ICCV21(13308-13318)
IEEE DOI 2203
Representation learning, Interpolation, Dynamics, Transfer learning, Force, Network architecture, BibRef

Chen, Y.X.[Yu-Xin], Zhang, Z.Q.[Zi-Qi], Yuan, C.F.[Chun-Feng], Li, B.[Bing], Deng, Y.[Ying], Hu, W.M.[Wei-Ming],
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition,
ICCV21(13339-13348)
IEEE DOI 2203
Correlation, Network topology, Convolution, Computational modeling, Aggregates, Refining, Action and behavior recognition, BibRef

Nguyen, X.S.[Xuan Son],
GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition,
ICCV21(13359-13369)
IEEE DOI 2203
Geometry, Manifolds, Symmetric matrices, Neural networks, Gaussian distribution, Action and behavior recognition, Video analysis and understanding BibRef

Huynh-The, T.[Thien], Hua, C.H.[Cam-Hao], Tu, N.A.[Nguyen Anh], Kim, D.S.[Dong-Seong],
Space-Time Skeletal Analysis with Jointly Dual-Stream ConvNet for Action Recognition,
DICTA20(1-7)
IEEE DOI 2201
Training, Image recognition, Dynamics, Skeleton, Kernel, Action recognition, convolutional network, 3D skeleton data BibRef

Gupta, P.[Pranay], Sharma, D.[Divyanshu], Sarvadevabhatla, R.K.[Ravi Kiran],
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition,
ICIP21(439-443)
IEEE DOI 2201
Training, Visualization, Image recognition, Syntactics, Skeleton, Task analysis, ZSL, skeleton action recognition, VAE, deep learning, language and vision BibRef

Lie, W.N.[Wen-Nung], Huang, Y.J.[Yong-Jhu], Chiang, J.C.[Jui-Chiu], Fang, Z.Y.[Zhen-Yu],
High-Order Joint Information Input for Graph Convolutional Network Based Action Recognition,
ICIP21(1064-1068)
IEEE DOI 2201
Couplings, Protocols, Fuses, Convolution, Image edge detection, Deep learning, action recognition, graph convolutional network, 3D human skeleton BibRef

Häring, S.[Simon], Memmesheimer, R.[Raphael], Paulus, D.[Dietrich],
Action Segmentation on Representations of Skeleton Sequences Using Transformer Networks,
ICIP21(3053-3057)
IEEE DOI 2201
Image segmentation, Image recognition, Image coding, Art, Motion segmentation, Estimation, Action segmentation, Transformer, object detection BibRef

Panousis, K.P.[Konstantinos P.], Chatzis, S.[Soritios], Theodoridis, S.[Sergios],
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition,
ISVC21(II:67-80).
Springer DOI 2112
BibRef

Diao, Y.F.[Yun-Feng], Shao, T.J.[Tian-Jia], Yang, Y.L.[Yong-Liang], Zhou, K.[Kun], Wang, H.[He],
BASAR:Black-box Attack on Skeletal Action Recognition,
CVPR21(7593-7603)
IEEE DOI 2111
Activity recognition, Robustness, Data models BibRef

Wang, H.[He], He, F.X.[Fei-Xiang], Peng, Z.X.[Zhe-Xi], Shao, T.J.[Tian-Jia], Yang, Y.L.[Yong-Liang], Zhou, K.[Kun], Hogg, D.[David],
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack,
CVPR21(14651-14660)
IEEE DOI 2111
Surveillance, Robustness, Pattern recognition, Autonomous vehicles BibRef

Lohit, S.[Suhas], Anirudh, R.[Rushil], Turaga, P.[Pavan],
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization,
WACV21(2341-2350)
IEEE DOI 2106
Manifolds, Tracking, Time series analysis, Training data, Transforms, Trajectory BibRef

Yang, D.[Di], Dai, R.[Rui], Wang, Y.[Yaohui], Mallick, R.[Rupayan], Minciullo, L.[Luca], Francesca, G.[Gianpiero], Brémond, F.[François],
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos,
WACV21(2362-2371)
IEEE DOI 2106
Pose estimation, Boosting, Skeleton, Data models BibRef

Cai, J.M.[Jin-Miao], Jiang, N.[Nianjuan], Han, X.G.[Xiao-Guang], Jia, K.[Kui], Lu, J.B.[Jiang-Bo],
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition,
WACV21(2734-2743)
IEEE DOI 2106
Visualization, Skeleton, Character recognition, Optical flow BibRef

Obinata, Y.[Yuya], Yamamoto, T.[Takuma],
Temporal Extension Module for Skeleton-Based Action Recognition,
ICPR21(534-540)
IEEE DOI 2105
Convolution, Biological system modeling, Feature extraction, Skeleton, Pattern recognition, Optimization, Action recognition, Kinetics-Skeleton BibRef

Ban Teng, M.L.[Michael Lao], Wu, Z.Y.[Zhi-Yong],
Channel-Wise Dense Connection Graph Convolutional Network for Skeleton-Based Action Recognition,
ICPR21(3799-3806)
IEEE DOI 2105
Legged locomotion, Adaptation models, Time series analysis, Feature extraction, Data models, Robustness, Kinetic theory BibRef

Nam, S.[Suekyeong], Lee, S.K.[Seung-Kyu],
JT-MGCN: Joint-temporal Motion Graph Convolutional Network for Skeleton-Based Action Recognition,
ICPR21(6383-6390)
IEEE DOI 2105
Correlation, Skeleton, Pattern recognition BibRef

Heidari, N.[Negar], Iosifidis, A.[Alexandros],
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition,
ICPR21(7907-7914)
IEEE DOI 2105
Network topology, Computational modeling, Benchmark testing, Skeleton, Data models, Distance measurement, Pattern recognition BibRef

Chen, L.[Lian], Lu, K.[Ke], Gao, P.C.[Peng-Cheng], Xue, J.[Jian], Wang, J.B.[Jin-Bao],
A Novel Multi-feature Skeleton Representation for 3d Action Recognition,
IUC20(365-379).
Springer DOI 2103
BibRef

Shiraki, K.[Katsutoshi], Hirakawa, T.[Tsubasa], Yamashita, T.[Takayoshi], Fujiyoshi, H.[Hironobu],
Spatial Temporal Attention Graph Convolutional Networks with Mechanics-stream for Skeleton-based Action Recognition,
ACCV20(V:341-357).
Springer DOI 2103
BibRef

Radek, S.,
Skeleton Action Recognition Based on Singular Value Decomposition,
ICIP20(1831-1835)
IEEE DOI 2011
Protocols, Skeleton, Symmetric matrices, Robustness, Trajectory, Feature extraction, singular value decomposition BibRef

Singh, I., Zhu, X., Greenspan, M.,
Multi-Modal Fusion With Observation Points For Skeleton Action Recognition,
ICIP20(1781-1785)
IEEE DOI 2011
Joints, Bones, Training, multimodal fusion BibRef

Su, K., Liu, X., Shlizerman, E.,
PREDICT CLUSTER: Unsupervised Skeleton Based Action Recognition,
CVPR20(9628-9637)
IEEE DOI 2008
Decoding, Skeleton, Training, Task analysis, Cameras BibRef

Zhang, X., Xu, C., Tao, D.,
Context Aware Graph Convolution for Skeleton-Based Action Recognition,
CVPR20(14321-14330)
IEEE DOI 2008
Convolution, Context-aware services, Computational modeling, Context modeling, Task analysis, Skeleton, Feature extraction BibRef

Yang, Z., Zhu, W., Wu, W., Qian, C., Zhou, Q., Zhou, B., Loy, C.C.,
TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting,
CVPR20(5305-5314)
IEEE DOI 2008
Skeleton, Perturbation methods, Rendering (computer graphics), Decoding BibRef

Cui, Q., Sun, H., Yang, F.,
Learning Dynamic Relationships for 3D Human Motion Prediction,
CVPR20(6518-6526)
IEEE DOI 2008
Skeleton, Training, Adaptation models, Predictive models, Solid modeling, Dynamics BibRef

Corona, E., Pumarola, A., Alenyà, G., Moreno-Noguer, F.,
Context-Aware Human Motion Prediction,
CVPR20(6990-6999)
IEEE DOI 2008
Predictive models, Task analysis, Skeleton, Recurrent neural networks, Semantics, Context modeling BibRef

Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.,
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition,
CVPR20(140-149)
IEEE DOI 2008
Feature extraction, Joints, Robustness, Bones, Pattern recognition, Correlation BibRef

Wang, Y., Xiao, Y., Xiong, F., Jiang, W., Cao, Z., Zhou, J.T., Yuan, J.,
3DV: 3D Dynamic Voxel for Action Recognition in Depth Video,
CVPR20(508-517)
IEEE DOI 2008
Machine learning, Dynamics, Solid modeling, Pattern recognition, Skeleton BibRef

Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., Zheng, N.,
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition,
CVPR20(1109-1118)
IEEE DOI 2008
Skeleton, Semantics, Indexes, Neural networks, Computational modeling, Correlation BibRef

Huang, J.Q.[Jun-Qin], Huang, Z.H.[Zhen-Huan], Xiang, X.[Xiang], Gong, X.[Xuan], Zhang, B.C.[Bao-Chang],
Long-Short Graph Memory Network for Skeleton-Based Action Recognition,
WACV20(634-641)
IEEE DOI 2006
Feature extraction, Convolution, Skeleton, Calibration, Data models, Data mining, Neural networks BibRef

Raj, N.B.[N. Bharath], Subramanian, A.[Anand], Ravichandran, K.[Kashyap], Venkateswaran, N.,
Exploring Techniques to Improve Activity Recognition using Human Pose Skeletons,
WACVWS20(165-172)
IEEE DOI 2006
Skeleton, Feature extraction, Activity recognition, Mathematical model, Robustness, Pose estimation, Training BibRef

Zhao, R., Wang, K., Su, H., Ji, Q.,
Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition,
ICCV19(6881-6891)
IEEE DOI 2004
Bayes methods, feature extraction, graph theory, image motion analysis, image representation, Kernel BibRef

Yan, S., Li, Z., Xiong, Y., Yan, H., Lin, D.,
Convolutional Sequence Generation for Skeleton-Based Action Synthesis,
ICCV19(4393-4401)
IEEE DOI 2004
autoregressive processes, convolutional neural nets, Gaussian processes, graph theory, image motion analysis, Generative adversarial networks BibRef

Szczapa, B., Daoudi, M., Berretti, S., del Bimbo, A., Pala, P., Massart, E.,
Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition,
HBU19(1241-1250)
IEEE DOI 2004
curve fitting, feature extraction, image motion analysis, image representation, image sequences, Skeleton BibRef

Hakim, T., Shimshoni, I.,
A-MAL: Automatic Motion Assessment Learning from Properly Performed Motions in 3D Skeleton Videos,
CVPM19(1589-1598)
IEEE DOI 2004
image motion analysis, image segmentation, learning (artificial intelligence), medical image processing, fma BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Chen, X.[Xu], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng], Tian, Q.[Qi],
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition,
CVPR19(3590-3598).
IEEE DOI 2002
BibRef

Laraba, S.[Sohaib], Tilmanne, J.[Joëlle], Dutoit, T.[Thierry],
Leveraging Pre-trained CNN Models for Skeleton-based Action Recognition,
CVS19(612-626).
Springer DOI 1912
BibRef

Khamsehashari, R., Gadzicki, K., Zetzsche, C.,
Deep Residual Temporal Convolutional Networks for Skeleton-based Human Action Recognition,
CVS19(376-385).
Springer DOI 1912
BibRef

Ye, F., Tang, H., Wang, X., Liang, X.,
Joints Relation Inference Network for Skeleton-Based Action Recognition,
ICIP19(16-20)
IEEE DOI 1910
Action Recognition, Relation Inference, Graph Convolutional Network, Skeleton BibRef

Kao, J., Ortega, A., Tian, D., Mansour, H., Vetro, A.,
Graph Based Skeleton Modeling for Human Activity Analysis,
ICIP19(2025-2029)
IEEE DOI 1910
Human activity analysis, graph-based representation, motion capture data, 3D action recognition BibRef

Tang, Y.S.[Yan-Song], Tian, Y.[Yi], Lu, J.W.[Ji-Wen], Li, P.Y.[Pei-Yang], Zhou, J.[Jie],
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition,
CVPR18(5323-5332)
IEEE DOI 1812
Joints, Videos, Biological system modeling BibRef

Cui, R., Zhu, A., Zhang, S., Hua, G.,
Multi-source Learning for Skeleton-based Action Recognition Using Deep LSTM Networks,
ICPR18(547-552)
IEEE DOI 1812
Skeleton, Logic gates, Computational modeling, Torso, Legged locomotion, Cameras, Data mining, Machine Learning, Long Short-Term Memory BibRef

Khodabandeh, M., Joze, H.R.V., Zharkov, I., Pradeep, V.,
DIY Human Action Dataset Generation,
Joint18(1529-152910)
IEEE DOI 1812
Skeleton, Task analysis, Trajectory, Generators, Training BibRef

Sogi, N., Fukui, K.,
Action Recognition Method Based on Sets of Time Warped ARMA Models,
ICPR18(1773-1778)
IEEE DOI 1812
Observability, Hidden Markov models, Analytical models, Manifolds, Skeleton, Solid modeling BibRef

Xu, J., Tasaka, K., Yanagihara, H.,
Beyond Two-stream: Skeleton-based Three-stream Networks for Action Recognition in Videos,
ICPR18(1567-1573)
IEEE DOI 1812
Skeleton, Videos, Optical imaging, Agriculture BibRef

Rhif, M., Wannous, H., Farah, I.R.,
Action Recognition from 3D Skeleton Sequences using Deep Networks on Lie Group Features,
ICPR18(3427-3432)
IEEE DOI 1812
Feature extraction, Skeleton, Mathematical model, Tensile stress, Manifolds, Convolution BibRef

Demisse, G.G., Papadopoulos, K., Aouada, D., Ottersten, B.,
Pose Encoding for Robust Skeleton-Based Action Recognition,
Crowd18(301-3016)
IEEE DOI 1812
Noise measurement, Data models, Decoding, Cameras, Noise reduction, Training, Unsupervised learning BibRef

Simkanic, R.[Radek],
Matrix Descriptor of Changes (MDC): Activity Recognition Based on Skeleton,
ACIVS18(14-25).
Springer DOI 1810
BibRef

Noori, F.M.[Farzan Majeed], Wallace, B.[Benedikte], Uddin, M.Z.[M. Zia], Torresen, J.[Jim],
A Robust Human Activity Recognition Approach Using OpenPose, Motion Features, and Deep Recurrent Neural Network,
SCIA19(299-310).
Springer DOI 1906
BibRef

Uddin, M.Z.[M. Zia], Khaksar, W., Torresen, J.[Jim],
Activity Recognition Using Deep Recurrent Neural Network on Translation and Scale-Invariant Features,
ICIP18(475-479)
IEEE DOI 1809
Depth videos, segmentation, skeleton, Radon, RNN BibRef

Wang, Z.K.[Zhi-Kai], Zhang, C.Y.[Chong-Yang], Luo, W.[Wu], Lin, W.Y.[Wei-Yao],
Key Joints Selection and Spatiotemporal Mining for Skeleton-Based Action Recognition,
ICIP18(3458-3462)
IEEE DOI 1809
Histograms, Trajectory, Spatiotemporal phenomena, Skeleton, Encoding, Feature extraction, skeleton BibRef

Tsingalis, I., Vretos, N., Daras, P.,
Leveraging Skeleton Structure and Time Dependencies in the Scope of Action Recognition,
ICIP18(470-474)
IEEE DOI 1809
Skeleton, Feature extraction, Optimization, Standards, Noise measurement, Human Activity BibRef

Wang, B., Huang, L., Hoai, M.,
Active Vision for Early Recognition of Human Actions,
CVPR20(1078-1088)
IEEE DOI 2008
Cameras, Bandwidth, Learning (artificial intelligence), Robot sensing systems, Pattern recognition, Recurrent neural networks BibRef

Wang, B., Hoai, M.,
Predicting Body Movement and Recognizing Actions: An Integrated Framework for Mutual Benefits,
FG18(341-348)
IEEE DOI 1806
Dynamics, Forecasting, Recurrent neural networks, Robots, Skeleton, Trajectory, action early recognition, early detection BibRef

Das, S., Koperski, M., Bremond, F., Francesca, G.,
Action recognition based on a mixture of RGB and depth based skeleton,
AVSS17(1-6)
IEEE DOI 1806
CAD, feature extraction, image colour analysis, image recognition, learning (artificial intelligence), neural nets, Videos BibRef

Liu, M., He, Q., Liu, H.,
Fusing shape and motion matrices for view invariant action recognition using 3D skeletons,
ICIP17(3670-3674)
IEEE DOI 1803
Encoding, Matrix converters, Robustness, Shape, Skeleton, Training, 3D action recognition, skeleton sequence BibRef

Papadopoulos, K., Antunes, M., Aouada, D., Ottersten, B.,
Enhanced trajectory-based action recognition using human pose,
ICIP17(1807-1811)
IEEE DOI 1803
Computational modeling, Feature extraction, Heating systems, Histograms, Skeleton, Standards, Trajectory, Action recognition, spatio-temporal features BibRef

Wei, S.H.[Sheng-Hua], Song, Y.H.[Yong-Hong], Zhang, Y.L.[Yuan-Lin],
Human skeleton tree recurrent neural network with joint relative motion feature for skeleton based action recognition,
ICIP17(91-95)
IEEE DOI 1803
Acceleration, Feature extraction, Logic gates, Neurons, Recurrent neural networks, Shoulder, Skeleton, Action recognition, skeleton joints BibRef

Lee, I., Kim, D., Kang, S., Lee, S.,
Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks,
ICCV17(1012-1020)
IEEE DOI 1802
feature extraction, image motion analysis, image recognition, image representation, learning (artificial intelligence), BibRef

Huang, Z., Wan, C., Probst, T., Van Gool, L.J.[Luc J.],
Deep Learning on Lie Groups for Skeleton-Based Action Recognition,
CVPR17(1243-1252)
IEEE DOI 1711
Machine learning, Manifolds, Neural networks, Skeleton, Transforms BibRef

Wang, P.[Pei], Yuan, C.F.[Chun-Feng], Hu, W.M.[Wei-Ming], Li, B.[Bing], Zhang, Y.N.[Yan-Ning],
Graph Based Skeleton Motion Representation and Similarity Measurement for Action Recognition,
ECCV16(VII: 370-385).
Springer DOI 1611
BibRef

Ubalde, S., Gómez-Fernández, F., Goussies, N.A., Mejail, M.,
Skeleton-based action recognition using Citation-kNN on bags of time-stamped pose descriptors,
ICIP16(3051-3055)
IEEE DOI 1610
Hafnium BibRef

Mavroudi, E., Bhaskara, D., Sefati, S., Ali, H., Vidal, R.,
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding,
WACV18(1558-1567)
IEEE DOI 1806
feature extraction, gesture recognition, image classification, image representation, image segmentation, Task analysis BibRef

Mavroudi, E.[Effrosyni], Bindal, P.[Prashast], Vidal, R.[René],
Actor-Centric Tubelets for Real-Time Activity Detection in Extended Videos,
Activity22(172-181)
IEEE DOI 2202
Visualization, Tracking, Surveillance, Focusing, Object detection, Real-time systems, Graph neural networks BibRef

Mavroudi, E., Tao, L., Vidal, R.,
Deep Moving Poselets for Video Based Action Recognition,
WACV17(111-120)
IEEE DOI 1609
BibRef
Earlier: A2, A3, Only:
Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition,
ChaLearnDec15(303-311)
IEEE DOI 1602
Feature extraction, Hip, Legged locomotion, Shoulder, Support vector machines, Trajectory, Computational modeling BibRef

Batabyal, T.[Tamal], Chattopadhyay, T.[Tanushyam], Mukherjee, D.P.[Dipti Prasad],
Action recognition using joint coordinates of 3D skeleton data,
ICIP15(4107-4111)
IEEE DOI 1512
Covariance; Kinect; Local Linear Embedding BibRef

Meshry, M., Hussein, M.E.[Mohamed E.], Torki, M.[Marwan],
Linear-time online action detection from 3D skeletal data using bags of gesturelets,
WACV16(1-9)
IEEE DOI 1606
Feature extraction BibRef

Sharaf, A.[Amr], Torki, M.[Marwan], Hussein, M.E.[Mohamed E.], El-Saban, M.[Motaz],
Real-Time Multi-scale Action Detection from 3D Skeleton Data,
WACV15(998-1005)
IEEE DOI 1503
Detectors BibRef

Evangelidis, G.[Georgios], Singh, G.[Gurkirt], Horaud, R.[Radu],
Skeletal Quads: Human Action Recognition Using Joint Quadruples,
ICPR14(4513-4518)
IEEE DOI 1412
Accuracy; Joints; Kernel; Training; Vectors BibRef

Chaudhry, R.[Rizwan], Ofli, F.[Ferda], Kurillo, G.[Gregorij], Bajcsy, R.[Ruzena], Vidal, R.[Rene],
Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition,
HAU3D13(471-478)
IEEE DOI 1309
BibRef

Bakken, R.H.[Rune Havnung], Hilton, A.[Adrian],
Real-Time Pose Estimation Using Constrained Dynamics,
AMDO12(37-46).
Springer DOI 1208
BibRef

Bakken, R.H., Eliassen, L.M.,
Real-time 3D skeletonisation in computer vision-based human pose estimation using GPGPU,
IPTA12(61-67)
IEEE DOI 1503
graphics processing units BibRef

Karali, A.[Abubakrelsedik], El Helw, M.[Mohamed],
Motion History of Skeletal Volumes for Human Action Recognition,
ISVC12(II: 135-144).
Springer DOI 1209
BibRef

Xu, R.[Ran], Agarwal, P.[Priyanshu], Kumar, S.[Suren], Krovi, V.N.[Venkat N.], Corso, J.J.[Jason J.],
Combining Skeletal Pose with Local Motion for Human Activity Recognition,
AMDO12(114-123).
Springer DOI 1208
BibRef

Yoon, S.M.[Sang Min], Kuijper, A.[Arjan],
Human Action Recognition Using Segmented Skeletal Features,
ICPR10(3740-3743).
IEEE DOI 1008
BibRef
And:
3D Human Action Recognition Using Model Segmentation,
ICIAR10(I: 189-199).
Springer DOI 1006
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Articulatd Action Recognition .