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
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
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
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
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, 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
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
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
Fan, Z.X.[Zhao-Xuan],
Zhao, X.[Xu],
Lin, T.W.[Tian-Wei],
Su, H.S.[Hai-Sheng],
Attention-Based Multiview Re-Observation Fusion Network for Skeletal
Action Recognition,
MultMed(21), No. 2, February 2019, pp. 363-374.
IEEE DOI
1902
Skeleton, Nonhomogeneous media, Fuses, Task analysis,
Visualization, Pose estimation,
long short-term memory (LSTM)
BibRef
Su, H.S.[Hai-Sheng],
Zhao, X.[Xu],
Lin, T.W.[Tian-Wei],
Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action
Localization,
ACCV18(II:558-574).
Springer DOI
1906
BibRef
Liu, S.M.[Shu-Ming],
Zhao, X.[Xu],
Su, H.S.[Hai-Sheng],
Hu, Z.L.[Zhi-Lan],
TSI: Temporal Scale Invariant Network for Action Proposal Generation,
ACCV20(V:530-546).
Springer DOI
2103
BibRef
Lin, T.W.[Tian-Wei],
Zhao, X.[Xu],
Su, H.S.[Hai-Sheng],
Wang, C.J.[Chong-Jing],
Yang, M.[Ming],
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation,
ECCV18(II: 3-21).
Springer DOI
1810
BibRef
Lin, T.W.[Tian-Wei],
Zhao, X.[Xu],
Su, H.S.[Hai-Sheng],
Joint Learning of Local and Global Context for Temporal Action
Proposal Generation,
CirSysVideo(30), No. 12, December 2020, pp. 4899-4912.
IEEE DOI
2012
Proposals, Videos, Task analysis, Reliability, Convolution,
Object detection, Robots, Temporal action proposal generation,
untrimmed video
BibRef
Qing, Z.W.[Zhi-Wu],
Su, H.S.[Hai-Sheng],
Gan, W.H.[Wei-Hao],
Wang, D.L.[Dong-Liang],
Wu, W.[Wei],
Wang, X.[Xiang],
Qiao, Y.[Yu],
Yan, J.J.[Jun-Jie],
Gao, C.X.[Chang-Xin],
Sang, N.[Nong],
Temporal Context Aggregation Network for Temporal Action Proposal
Refinement,
CVPR21(485-494)
IEEE DOI
2111
Location awareness, Benchmark testing,
Reliability engineering, Proposals, Task analysis
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,
Solid modeling, Skeleton, Target recognition,
discriminative skeleton-based action recognition
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
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
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.
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
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.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
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.H.[Yu-Han],
Zhao, Z.F.[Zhi-Fu],
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
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IEICE(E103-D), No. 6, June 2020, pp. 1217-1225.
WWW Link.
2006
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
BibRef
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.
WWW Link.
2008
BibRef
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
Zhang, T.[Tong],
Zheng, W.M.[Wen-Ming],
Cui, Z.[Zhen],
Zong, Y.[Yuan],
Li, C.L.[Chao-Long],
Zhou, X.Y.[Xiao-Yan],
Yang, J.[Jian],
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
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
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
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
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
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
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
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
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
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
Xia, R.J.[Rong-Jie],
Li, Y.S.[Yan-Shan],
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
Sun, B.[Bin],
Wang, S.F.[Shao-Fan],
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
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
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
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
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
Zhang, Y.[Yu],
Sun, Z.H.[Zhong-Hua],
Dai, M.[Meng],
Feng, J.C.[Jin-Chao],
Jia, K.[Kebin],
Cross-Scale Spatiotemporal Refinement Learning for Skeleton-Based
Action Recognition,
SPLetters(31), 2024, pp. 441-445.
IEEE DOI
2402
Spatiotemporal phenomena, Semantics, Convolution,
Feature extraction, Correlation, Solid modeling, Joints,
cross-scale fusion
BibRef
Xu, L.Y.[Lei-Yang],
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.Y.[Kun-Yu],
Roitberg, A.[Alina],
Yang, K.L.[Kai-Lun],
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.Y.[Qin-Yang],
Liu, C.J.[Cheng-Ju],
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
Li, D.[Ding],
Tang, Y.Q.[Yong-Qiang],
Zhang, Z.Z.[Zhi-Zhong],
Zhang, W.S.[Wen-Sheng],
Cross-stream contrastive learning for self-supervised skeleton-based
action recognition,
IVC(135), 2023, pp. 104689.
Elsevier DOI
2306
Self-supervised learning, Contrastive learning,
Skeleton-based action recognition
BibRef
Liu, H.W.[Hao-Wei],
Liu, Y.C.[Yong-Cheng],
Chen, Y.X.[Yu-Xin],
Yuan, C.F.[Chun-Feng],
Li, B.[Bing],
Hu, W.M.[Wei-Ming],
TranSkeleton: Hierarchical Spatial-Temporal Transformer for
Skeleton-Based Action Recognition,
CirSysVideo(33), No. 8, August 2023, pp. 4137-4148.
IEEE DOI
2308
Transformers, Skeleton, Computational modeling, Feature extraction,
Convolution, Topology, Correlation,
long-range temporal dependencies
BibRef
Lv, J.R.[Jin-Rong],
Gong, X.[Xun],
Multi-Grained Temporal Segmentation Attention Modeling for
Skeleton-Based Action Recognition,
SPLetters(30), 2023, pp. 927-931.
IEEE DOI
2308
Skeleton, Feature extraction, Transformers, Topology,
Computational modeling, Motion segmentation, Task analysis, transformer
BibRef
Yang, S.Y.[Si-Yuan],
Liu, J.[Jun],
Lu, S.J.[Shi-Jian],
Hwa, E.M.[Er Meng],
Hu, Y.J.[Yong-Jian],
Kot, A.C.[Alex C.],
Self-Supervised 3D Action Representation Learning With Skeleton Cloud
Colorization,
PAMI(46), No. 1, January 2024, pp. 509-524.
IEEE DOI
2312
BibRef
Earlier: A1, A2, A3, A4, A6, Only:
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
Pang, C.[Chen],
Lu, X.Q.[Xue-Quan],
Lyu, L.[Lei],
Skeleton-Based Action Recognition Through Contrasting Two-Stream
Spatial-Temporal Networks,
MultMed(25), 2023, pp. 8699-8711.
IEEE DOI
2312
BibRef
Pan, Q.Z.[Qing-Zhe],
Zhao, Z.[Zhifu],
Xie, X.M.[Xue-Mei],
Li, J.A.[Jian-An],
Cao, Y.H.[Yu-Han],
Shi, G.M.[Guang-Ming],
View-Normalized and Subject-Independent Skeleton Generation for
Action Recognition,
CirSysVideo(33), No. 12, December 2023, pp. 7398-7412.
IEEE DOI
2312
BibRef
Xu, S.H.[Shi-Hao],
Rao, H.C.[Hao-Cong],
Hu, X.P.[Xi-Ping],
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
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
Guan, S.[Shannan],
Yu, X.[Xin],
Huang, W.[Wei],
Fang, G.[Gengfa],
Lu, H.Y.[Hai-Yan],
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action
Recognition,
IP(33), 2024, pp. 395-407.
IEEE DOI
2401
BibRef
Li, Y.H.[Yun-Heng],
Liu, K.Y.[Kai-Yuan],
Liu, S.L.[Sheng-Lan],
Feng, L.[Lin],
Qiao, H.[Hong],
Involving Distinguished Temporal Graph Convolutional Networks for
Skeleton-Based Temporal Action Segmentation,
CirSysVideo(34), No. 1, January 2024, pp. 647-660.
IEEE DOI
2401
BibRef
Wu, C.[Cong],
Wu, X.J.[Xiao-Jun],
Xu, T.Y.[Tian-Yang],
Shen, Z.[Zhongwei],
Kittler, J.V.[Josef V.],
Motion Complement and Temporal Multifocusing for Skeleton-Based
Action Recognition,
CirSysVideo(34), No. 1, January 2024, pp. 34-45.
IEEE DOI Code:
WWW Link.
2401
BibRef
He, X.T.[Xing-Tong],
Liu, X.[Xu],
Jiao, L.C.[Li-Cheng],
Global Shapes and Salient Joints Features Learning for Skeleton-Based
Action Recognition,
SPLetters(31), 2024, pp. 206-210.
IEEE DOI
2401
BibRef
Qiu, H.[Helei],
Hou, B.[Biao],
Multi-grained clip focus for skeleton-based action recognition,
PR(148), 2024, pp. 110188.
Elsevier DOI
2402
Action recognition, Skeleton, Multi-grain, Self-attention
BibRef
Song, W.F.[Wen-Feng],
Chu, T.[Tangli],
Li, S.[Shuai],
Li, N.N.[Nan-Nan],
Hao, A.[Aimin],
Qin, H.[Hong],
Joints-Centered Spatial-Temporal Features Fused Skeleton Convolution
Network for Action Recognition,
MultMed(26), 2024, pp. 4602-4616.
IEEE DOI
2403
Skeleton, Feature extraction, Convolution, Visualization,
Task analysis, Joints, Data mining, PDE diffusion
BibRef
Guo, T.Y.[Tian-Yu],
Liu, M.Y.[Meng-Yuan],
Liu, H.[Hong],
Wang, G.Q.[Guo-Quan],
Li, W.H.[Wen-Hao],
Improving self-supervised action recognition from extremely augmented
skeleton sequences,
PR(150), 2024, pp. 110333.
Elsevier DOI Code:
WWW Link.
2403
Self-supervised skeleton-based action recognition, Contrastive learning
BibRef
Wang, G.Q.[Guo-Quan],
Liu, M.Y.[Meng-Yuan],
Liu, H.[Hong],
Guo, P.[Peini],
Wang, T.[Ti],
Guo, J.W.[Jing-Wen],
Fan, R.J.[Rui-Jia],
Augmented skeleton sequences with hypergraph network for
self-supervised group activity recognition,
PR(152), 2024, pp. 110478.
Elsevier DOI Code:
WWW Link.
2405
Self-supervised learning, Skeleton-based group activity recognition,
Hypergraph learning
BibRef
Gu, C.Z.[Chun-Zhi],
Zhang, C.[Chao],
Kuriyama, S.[Shigeru],
Orientation-aware leg movement learning for action-driven human
motion prediction,
PR(150), 2024, pp. 110317.
Elsevier DOI
2403
Stochastic human motion prediction,
Motion transition learning, Deep generative model
BibRef
Yin, X.P.[Xin-Peng],
Zhong, J.Q.[Jian-Qi],
Lian, D.L.[De-Liang],
Cao, W.M.[Wen-Ming],
Spatiotemporal Progressive Inward-Outward Aggregation Network for
skeleton-based action recognition,
PR(150), 2024, pp. 110262.
Elsevier DOI
2403
Action recognition, Graph convolutional networks,
Progressive aggregation, Self-attention mechanism
BibRef
Myung, W.[Woomin],
Su, N.[Nan],
Xue, J.H.[Jing-Hao],
Wang, G.J.[Gui-Jin],
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based
Action Recognition,
IP(33), 2024, pp. 2477-2490.
IEEE DOI
2404
Convolution, Adaptation models, Topology, Deformable models,
Correlation, Convolutional neural networks, Laplace equations,
deformable convolution
BibRef
Shi, J.Y.[Jun-Yu],
Zhong, J.Q.[Jian-Qi],
Cao, W.M.[Wen-Ming],
Multi-Semantics Aggregation Network Based on the Dynamic-Attention
Mechanism for 3D Human Motion Prediction,
MultMed(26), 2024, pp. 5194-5206.
IEEE DOI
2404
Semantics, Dynamics, Convolution, Predictive models,
Feature extraction, Correlation, Frequency-domain analysis,
skeleton-based data processing
BibRef
Zhang, W.X.[Wen-Xian],
Scene context-aware graph convolutional network for skeleton-based
action recognition,
IET-CV(18), No. 3, 2024, pp. 343-354.
DOI Link
2404
graph theory, image classification, image recognition
BibRef
Li, C.K.[Chuan-Kun],
Li, S.[Shuai],
Gao, Y.B.[Yan-Bo],
Zhou, L.J.[Li-Juan],
Li, W.Q.[Wan-Qing],
Static graph convolution with learned temporal and channel-wise graph
topology generation for skeleton-based action recognition,
CVIU(244), 2024, pp. 104012.
Elsevier DOI
2405
Action recognition, Skeleton data, Static graph topology,
Graph convolutional networks
BibRef
Diao, Y.F.[Yun-Feng],
Wang, H.[He],
Shao, T.[Tianjia],
Yang, Y.L.[Yong-Liang],
Zhou, K.[Kun],
Hogg, D.[David],
Wang, M.[Meng],
Understanding the vulnerability of skeleton-based Human Activity
Recognition via black-box attack,
PR(153), 2024, pp. 110564.
Elsevier DOI
2405
Black-box attack, Skeletal action recognition,
Adversarial robustness, On-manifold adversarial samples
BibRef
Yang, S.Y.[Si-Yuan],
Liu, J.[Jun],
Lu, S.J.[Shi-Jian],
Hwa, E.M.[Er Meng],
Kot, A.C.[Alex C.],
One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton
Matching,
PAMI(46), No. 7, July 2024, pp. 5149-5156.
IEEE DOI
2406
Skeleton, Semantics, Task analysis, Optimal matching, Measurement,
Convolutional neural networks, Adaptation models,
skeleton action recognition
BibRef
Nan, H.[Hai],
Ye, Q.[Qilang],
Yu, Z.T.[Zi-Tong],
An, K.[Kang],
3sG: Three-stage guidance for indoor human action recognition,
IET-IPR(18), No. 8, 2024, pp. 2000-2010.
DOI Link
2406
Indoor is more fine-grained action.
convolutional neural nets
BibRef
Yang, D.[Di],
Wang, Y.H.[Yao-Hui],
Dantcheva, A.[Antitza],
Garattoni, L.[Lorenzo],
Francesca, G.[Gianpiero],
Brémond, F.[François],
View-Invariant Skeleton Action Representation Learning via Motion
Retargeting,
IJCV(132), No. 7, July 2024, pp. Pages2351-2366.
Springer DOI
2406
BibRef
Yang, D.[Di],
Wang, Y.H.[Yao-Hui],
Dantcheva, A.[Antitza],
Kong, Q.[Quan],
Garattoni, L.[Lorenzo],
Francesca, G.[Gianpiero],
Brémond, F.[François],
LAC: Latent Action Composition for Skeleton-based Action
Segmentation,
ICCV23(13633-13644)
IEEE DOI
2401
BibRef
Wang, L.[Lei],
Yang, S.M.[Shan-Min],
Zhang, J.W.[Jian-Wei],
Gu, S.[Song],
2D Human Skeleton Action Recognition Based on Depth Estimation,
IEICE(E108-D), No. 7, July 2024, pp. 869-877.
WWW Link.
2407
BibRef
Yu, B.X.B.[Bruce X. B.],
Liu, Y.[Yan],
Chan, K.C.C.[Keith C. C.],
Chen, C.W.[Chang Wen],
EGCN++: A New Fusion Strategy for Ensemble Learning in Skeleton-Based
Rehabilitation Exercise Assessment,
PAMI(46), No. 9, September 2024, pp. 6471-6485.
IEEE DOI
2408
Skeleton, Hidden Markov models, Data models, Ensemble learning,
Convolutional neural networks, Training, Human action evaluation,
ensemble learning
BibRef
Jang, S.J.[Sung-Jun],
Lee, H.[Heansung],
Kim, W.J.[Woo Jin],
Lee, J.[Jungho],
Woo, S.[Sungmin],
Lee, S.Y.[Sang-Youn],
Multi-Scale Structural Graph Convolutional Network for Skeleton-Based
Action Recognition,
CirSysVideo(34), No. 8, August 2024, pp. 7244-7258.
IEEE DOI
2408
Topology, Feature extraction, Correlation,
Convolutional neural networks, Convolution, Network topology,
link prediction
BibRef
Lee, J.[Jungho],
Lee, M.[Minhyeok],
Lee, D.[Dogyoon],
Lee, S.Y.[Sang-Youn],
Hierarchically Decomposed Graph Convolutional Networks for
Skeleton-Based Action Recognition,
ICCV23(10410-10419)
IEEE DOI Code:
WWW Link.
2401
BibRef
Wang, K.[Kun],
Cao, J.X.[Jiu-Xin],
Cao, B.W.[Bi-Wei],
Liu, B.[Bo],
EnsCLR: Unsupervised skeleton-based action recognition via ensemble
contrastive learning of representation,
CVIU(247), 2024, pp. 104076.
Elsevier DOI
2408
Unsupervised representation learning, Contrastive learning,
Skeleton-based action recognition
BibRef
Hu, R.T.[Ruo-Tong],
Wang, X.Z.[Xian-Zhi],
Chang, X.J.[Xiao-Jun],
Zhang, Y.L.[Yong-Le],
Hu, Y.Q.[Ye-Qi],
Liu, X.Y.[Xin-Yuan],
Yu, S.[Shusong],
CStrCRL: Cross-View Contrastive Learning Through Gated GCN With
Strong Augmentations for Skeleton Recognition,
CirSysVideo(34), No. 8, August 2024, pp. 6674-6685.
IEEE DOI Code:
WWW Link.
2408
Skeleton, Task analysis, Logic gates, Semantics,
Unsupervised learning, Data models, ST-GGCN
BibRef
Wang, L.[Lei],
Liu, J.[Jun],
Zheng, L.[Liang],
Gedeon, T.[Tom],
Koniusz, P.[Piotr],
Meet JEANIE: A Similarity Measure for 3D Skeleton Sequences via
Temporal-Viewpoint Alignment,
IJCV(132), No. 1, January 2024, pp. 4091-4122.
Springer DOI
2409
BibRef
Zhang, J.X.[Jia-Xu],
Tu, Z.G.[Zhi-Gang],
Weng, J.[Junwu],
Yuan, J.S.[Jun-Song],
Du, B.[Bo],
A Modular Neural Motion Retargeting System Decoupling Skeleton and
Shape Perception,
PAMI(46), No. 10, October 2024, pp. 6889-6904.
IEEE DOI
2409
Skeleton, Semantics, Geometry, Shape, Training, Logic gates,
Geometry perception, motion retargeting, skeleton topology
BibRef
Lei, S.Y.[Si-Yue],
Tang, B.[Bin],
Chen, Y.H.[Yan-Hua],
Zhao, M.F.[Ming-Fu],
Xu, Y.F.[Yi-Fei],
Long, Z.R.[Zou-Rong],
Temporal channel reconfiguration multi-graph convolution network for
skeleton-based action recognition,
IET-CV(18), No. 6, 2024, pp. 813-825.
DOI Link
2409
convolution, pose estimation
BibRef
Zhang, S.J.[Shao-Jie],
Yin, J.Q.[Jian-Qin],
Dang, Y.H.[Yong-Hao],
Fu, J.J.[Jia-Jun],
SiT-MLP: A Simple MLP With Point-Wise Topology Feature Learning for
Skeleton-Based Action Recognition,
CirSysVideo(34), No. 9, September 2024, pp. 8122-8134.
IEEE DOI Code:
WWW Link.
2410
Topology, Network topology, Transformers, Adaptation models,
Skeleton, Correlation,
spatial-temporal optimization
BibRef
Wang, X.H.[Xing-Han],
Mu, Y.D.[Ya-Dong],
Localized Linear Temporal Dynamics for Self-Supervised Skeleton
Action Recognition,
MultMed(26), 2024, pp. 10189-10199.
IEEE DOI
2410
Skeleton, Feature extraction, Dynamical systems, Task analysis,
Aerodynamics, Transforms, Neural networks, contrastive learning
BibRef
Cui, R.[Ran],
Wu, J.[Jingran],
Wang, X.[Xiang],
LG-AKD: Application of a lightweight GCN model based on adversarial
knowledge distillation to skeleton action recognition,
JVCIR(104), 2024, pp. 104286.
Elsevier DOI
2411
Action recognition, Adversarial neural networks, GCN,
Knowledge distillation, Skeleton
BibRef
Liu, Y.N.[Ya-Nan],
Li, Y.Q.[Yan-Qiu],
Zhang, H.[Hao],
Zhang, X.J.[Xue-Jie],
Xu, D.[Dan],
Decoupled Knowledge Embedded Graph Convolutional Network for
Skeleton-Based Human Action Recognition,
CirSysVideo(34), No. 10, October 2024, pp. 9445-9457.
IEEE DOI
2411
Skeleton, Knowledge engineering, Feature extraction, Computational modeling,
Topology, Computational efficiency, knowledge distillation
BibRef
Wang, L.[Lei],
Zhang, J.W.[Jian-Wei],
Yang, W.B.[Wen-Bing],
Gu, S.[Song],
Yang, S.[Shanmin],
2D human skeleton action recognition with spatial constraints,
IET-CV(18), No. 7, 2024, pp. 968-981.
DOI Link
2411
feature extraction, pattern recognition, video surveillance
BibRef
Zhang, F.[Fan],
Chongyang, D.[Ding],
Liu, K.[Kai],
Hong-Jin, L.[Liu],
Multi-scale skeleton simplification graph convolutional network for
skeleton-based action recognition,
IET-CV(18), No. 7, 2024, pp. 992-1003.
DOI Link
2411
computer vision, convolution, feature extraction,
neural net architecture, neural nets
BibRef
Mansouri, A.[Amine],
Bakir, T.[Toufik],
Elzaar, A.[Abdellah],
Improved semantic-guided network for skeleton-based action
recognition,
JVCIR(104), 2024, pp. 104281.
Elsevier DOI
2411
Deep learning, Human Action Recognition (HAR),
Convolutional Neural Networks (CNNs),
Attention mechanism
BibRef
Zhang, W.Y.[Wan-Ying],
Liu, M.Y.[Meng-Yuan],
Wang, X.[Xinshun],
Zhao, S.[Shen],
Wang, C.[Can],
CHAMP: A Large-Scale Dataset for Skeleton-Based Composite HumAn
Motion Prediction,
CirSysVideo(34), No. 10, October 2024, pp. 10063-10076.
IEEE DOI
2411
Task analysis, Predictive models, Skeleton, Legged locomotion,
Training data, Training, Decoding, Human motion prediction,
skeleton data
BibRef
Zhang, S.Q.[Shan-Qing],
Jiao, S.H.[Shu-Heng],
Chen, Y.J.[Yu-Jie],
Xu, J.Y.[Jia-Yi],
Action recognition algorithm based on skeleton graph with multiple
features and improved adjacency matrix,
IET-IPR(18), No. 13, 2024, pp. 4250-4262.
DOI Link
2411
adjacency matrix, graph convolutional networks, human action recognition,
multiscale features, skeleton graph, topological relations
BibRef
Martinelli, G.[Giulia],
Garau, N.[Nicola],
Bisagno, N.[Niccolò],
Conci, N.[Nicola],
All Skeletons are Created Equal! A Domain Adaptation Transformer to
Handle Multiple Topologies,
ICIP24(2716-2722)
IEEE DOI Code:
WWW Link.
2411
Training, Adaptation models, Network topology, Annotations,
Transformers, Topology, Skeletons, Poses, Topologies
BibRef
Sahbi, H.[Hichem],
One-Shot Multi-Rate Pruning Of Graph Convolutional Networks for
Skeleton-Based Recognition,
ICIP24(2445-2451)
IEEE DOI
2411
Training, Image recognition, Accuracy,
Graph convolutional networks, Network topology, Task analysis,
skeleton-based recognition
BibRef
Mitsuzumi, Y.[Yu],
Kimura, A.[Akisato],
Irie, G.[Go],
Nakazawa, A.[Atsushi],
Cross-Action Cross-Subject Skeleton Action Recognition Via
Simultaneous Action-Subject Learning with Two-Step Feature Removal,
ICIP24(2182-2186)
IEEE DOI
2411
Training, Image recognition, Accuracy, Target recognition,
Data augmentation, Bones, Mutual information, Action Recognition,
Disentanglement
BibRef
Akremi, M.S.[Mohamed Sanim],
Neji, N.[Najett],
Tabia, H.[Hedi],
Temporal-Spatial SPDAGG Network For Skeleton-Based Human Action
Recognition From Aerial Perspectives,
ICIP24(1384-1390)
IEEE DOI
2411
Face recognition, Autonomous aerial vehicles, Robustness,
Behavioral sciences, Human activity recognition, Gauss Aggregation
BibRef
Wang, X.S.[Xin-Shun],
Fang, Z.B.[Zhong-Bin],
Li, X.[Xia],
Li, X.T.[Xiang-Tai],
Chen, C.[Chen],
Liu, M.Y.[Meng-Yuan],
Skeleton-in-Context: Unified Skeleton Sequence Modeling with
In-Context Learning,
CVPR24(2436-2446)
IEEE DOI
2410
Training, Solid modeling, Silicon carbide, Computational modeling,
Pose estimation, Predictive models, in-context learning,
skeleton modeling
BibRef
Abdelfattah, M.[Mohamed],
Hassan, M.[Mariam],
Alahi, A.[Alexandre],
MaskCLR: Attention-Guided Contrastive Learning for Robust Action
Representation Learning,
CVPR24(18678-18687)
IEEE DOI Code:
WWW Link.
2410
Training, Accuracy, Current transformers, Perturbation methods,
Semantics, Contrastive learning, Skeleton,
contrastive learning
BibRef
Noor, N.[Nadhira],
Jametoni, F.[Fabianaugie],
Kim, J.[Jinbeom],
Hong, H.[Hyunsu],
Park, I.K.[In Kyu],
Efficient Skeleton-Based Action Recognition for Real-Time Embedded
Systems,
MobileAI24(5889-5897)
IEEE DOI
2410
Embedded systems, Accuracy, Quantization (signal), Surveillance,
Memory management, Network architecture, Real-time systems,
convolutional neural network
BibRef
Fukushi, K.[Kenichiro],
Nozaki, Y.[Yoshitaka],
Nishihara, K.[Kosuke],
Nakahara, K.[Kentaro],
Few-shot generative model for skeleton-based human action synthesis
using cross-domain adversarial learning,
WACV24(3934-3943)
IEEE DOI
2404
Training, Data visualization, Entropy, Generators, Data models,
Adversarial machine learning, Algorithms, Adversarial learning.
BibRef
Lerch, D.J.[David J.],
Zhong, Z.Y.[Ze-Yun],
Martin, M.[Manuel],
Voit, M.[Michael],
Beyerer, J.[Jürgen],
Unsupervised 3D Skeleton-Based Action Recognition using
Cross-Attention with Conditioned Generation Capabilities,
RWSurvil24(202-211)
IEEE DOI
2404
Training, Surveillance, Noise reduction, Noise, Transformers, Skeleton
BibRef
Cormier, M.[Mickael],
Schmid, Y.[Yannik],
Beyerer, J.[Jürgen],
Enhancing Skeleton-Based Action Recognition in Real-World Scenarios
Through Realistic Data Augmentation,
RWSurvil24(300-309)
IEEE DOI Code:
WWW Link.
2404
Pose estimation, Data augmentation, Autonomous aerial vehicles,
Skeleton, Libraries
BibRef
Zhuang, Z.H.[Zhi-Han],
Li, Y.[Yuan],
Du, S.L.[Song-Lin],
Ikenaga, T.[Takeshi],
Intra-frame Skeleton Constraints Modeling and Grouping Strategy Based
Multi-Scale Graph Convolution Network for 3D Human Motion Prediction,
MVA23(1-5)
DOI Link
2403
Solid modeling, Convolution, Machine vision,
Biological system modeling, Predictive models, Feature extraction
BibRef
Zhu, Y.S.[Yi-Sheng],
Han, H.[Hu],
Yu, Z.T.[Zheng-Tao],
Liu, G.C.[Guang-Can],
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based
Action Recognition,
ICCV23(13867-13876)
IEEE DOI Code:
WWW Link.
2401
BibRef
Lee, J.[Jungho],
Lee, M.[Minhyeok],
Cho, S.[Suhwan],
Woo, S.[Sungmin],
Jang, S.J.[Sung-Jun],
Lee, S.Y.[Sang-Youn],
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action
Recognition,
ICCV23(10221-10230)
IEEE DOI Code:
WWW Link.
2401
BibRef
Xu, C.X.[Chen-Xin],
Tan, R.T.[Robby T.],
Tan, Y.H.[Yu-Hong],
Chen, S.[Siheng],
Wang, X.C.[Xin-Chao],
Wang, Y.F.[Yan-Feng],
Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction,
ICCV23(9475-9486)
IEEE DOI Code:
WWW Link.
2401
BibRef
Yan, H.[Hong],
Liu, Y.[Yang],
Wei, Y.[Yushen],
Li, Z.[Zhen],
Li, G.B.[Guan-Bin],
Lin, L.[Liang],
SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence
Pre-training,
ICCV23(5583-5595)
IEEE DOI Code:
WWW Link.
2401
BibRef
Duan, H.D.[Hao-Dong],
Xu, M.Z.[Ming-Ze],
Shuai, B.[Bing],
Modolo, D.[Davide],
Tu, Z.W.[Zhuo-Wen],
Tighe, J.[Joseph],
Bergamo, A.[Alessandro],
SkeleTR: Towards Skeleton-based Action Recognition in the Wild,
ICCV23(13588-13598)
IEEE DOI
2401
BibRef
Guo, J.W.[Jing-Wen],
Liu, H.[Hong],
Sun, S.T.[Shi-Tong],
Guo, T.Y.[Tian-Yu],
Zhang, M.[Min],
Si, C.Y.[Chen-Yang],
FSAR: Federated Skeleton-based Action Recognition with Adaptive
Topology Structure and Knowledge Distillation,
ICCV23(10366-10376)
IEEE DOI
2401
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,
Skeleton, Videos
BibRef
Wang, G.Q.[Guo-Quan],
Liu, H.[Hong],
Guo, T.Y.[Tian-Yu],
Guo, J.W.[Jing-Wen],
Wang, T.[Ti],
Li, Y.[Yidi],
Self-Supervised 3D Skeleton Representation Learning with Active
Sampling and Adaptive Relabeling for Action Recognition,
ICIP23(56-60)
IEEE DOI
2312
BibRef
He, R.[Rong],
Tang, Y.S.[Yan-Song],
Li, X.[Xiu],
Lu, J.W.[Ji-Wen],
Context-Aware Inpainter-Refiner for Skeleton-Based Human Motion
Completion,
ICIP23(296-300)
IEEE DOI
2312
BibRef
Wang, L.[Lei],
Koniusz, P.[Piotr],
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action
Recognition,
CVPR23(5620-5631)
IEEE DOI
2309
BibRef
Lin, L.[Lilang],
Zhang, J.H.[Jia-Hang],
Liu, J.Y.[Jia-Ying],
Actionlet-Dependent Contrastive Learning for Unsupervised
Skeleton-Based Action Recognition,
CVPR23(2363-2372)
IEEE DOI
2309
BibRef
Hachiuma, R.[Ryo],
Sato, F.[Fumiaki],
Sekii, T.[Taiki],
Unified Keypoint-Based Action Recognition Framework via Structured
Keypoint Pooling,
CVPR23(22962-22971)
IEEE DOI
2309
BibRef
Sato, F.[Fumiaki],
Hachiuma, R.[Ryo],
Sekii, T.[Taiki],
Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained
Deep Skeleton Features,
CVPR23(6471-6480)
IEEE DOI
2309
BibRef
Wang, X.H.[Xing-Han],
Xu, X.[Xin],
Mu, Y.D.[Ya-Dong],
Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding
for Skeleton-Based Action Recognition,
CVPR23(10597-10607)
IEEE DOI
2309
BibRef
Zhou, H.Y.[Huan-Yu],
Liu, Q.J.[Qing-Jie],
Wang, Y.H.[Yun-Hong],
Learning Discriminative Representations for Skeleton Based Action
Recognition,
CVPR23(10608-10617)
IEEE DOI
2309
BibRef
Shah, A.[Anshul],
Roy, A.[Aniket],
Shah, K.[Ketul],
Mishra, S.[Shlok],
Jacobs, D.[David],
Cherian, A.[Anoop],
Chellappa, R.[Rama],
HaLP: Hallucinating Latent Positives for Skeleton-based
Self-Supervised Learning of Actions,
CVPR23(18846-18856)
IEEE DOI
2309
BibRef
Deyzel, M.[Michael],
Theart, R.P.[Rensu P.],
One-shot skeleton-based action recognition on strength and
conditioning exercises,
CVSports23(5169-5178)
IEEE DOI
2309
BibRef
Wang, L.[Lei],
Koniusz, P.[Piotr],
Temporal-viewpoint Transportation Plan for Skeletal Few-shot Action
Recognition,
ACCV22(IV:307-326).
Springer DOI
2307
BibRef
Zhai, X.L.[Xiao-Lin],
Hu, Z.X.[Zheng-Xi],
Yang, D.Y.[Ding-Ye],
Zhou, L.[Lei],
Liu, J.[Jingtai],
Spatial Temporal Network for Image and Skeleton Based Group Activity
Recognition,
ACCV22(IV:329-346).
Springer DOI
2307
BibRef
Gao, Z.M.[Zhi-Min],
Wang, P.[Peitao],
Lv, P.[Pei],
Jiang, X.H.[Xiao-Heng],
Liu, Q.D.[Qi-Dong],
Wang, P.[Pichao],
Xu, M.L.[Ming-Liang],
Li, W.I.[Wanq-Ing],
Focal and Global Spatial-temporal Transformer for Skeleton-based Action
Recognition,
ACCV22(IV:155-171).
Springer DOI
2307
BibRef
Qin, Z.Y.[Zhen-Yue],
Ji, P.[Pan],
Kim, D.[Dongwoo],
Liu, Y.[Yang],
Anwar, S.[Saeed],
Gedeon, T.[Tom],
Strengthening Skeletal Action Recognizers via Leveraging Temporal
Patterns,
RealWorld22(577-593).
Springer DOI
2304
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
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
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
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
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, Autonomous vehicles
BibRef
Lu, Z.Z.[Zheng-Zhi],
Wang, H.[He],
Chang, Z.[Ziyi],
Yang, G.[Guoan],
Shum, H.P.H.[Hubert P. H.],
Hard No-Box Adversarial Attack on Skeleton-Based Human Action
Recognition with Skeleton-Motion-Informed Gradient,
ICCV23(4574-4583)
IEEE DOI
2401
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, Optimization, Action recognition,
Kinetics-Skeleton
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
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
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, Skeleton
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
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
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
Hiraoka, Y.[Yutaro],
Fukui, K.[Kazuhiro],
Deep Randomized Time Warping for Action Recognition,
MVA23(1-5)
DOI Link
2403
Machine vision, Dynamics, Feature extraction, Pattern matching
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
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
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
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
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
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
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 -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Action Recognition, Neural Nets for Skeletal Representations .