Laptev, I.[Ivan],
Caputo, B.[Barbara],
Schuldt, C.[Christian],
Lindeberg, T.[Tony],
Local velocity-adapted motion events for spatio-temporal recognition,
CVIU(108), No. 3, December 2007, pp. 207-229.
Elsevier DOI
0711
BibRef
Earlier: A3, A1, A2, Only:
Recognizing human actions: a local SVM approach,
ICPR04(III: 32-36).
IEEE DOI
0409
Dataset, Actions.
WWW Link. Motion; Local features; Motion descriptors; Matching; Velocity adaptation;
Action recognition; Learning; SVM
BibRef
Niebles, J.C.[Juan Carlos],
Wang, H.C.[Hong-Cheng],
Fei-Fei, L.[Li],
Unsupervised Learning of Human Action Categories Using Spatial-Temporal
Words,
IJCV(79), No. 3, September 2008, pp. xx-yy.
Springer DOI
0806
BibRef
BMVC06(III:1249).
PDF File.
0609
BibRef
Fan, L.X.[Lin-Xi],
Buch, S.[Shyamal],
Wang, G.Z.[Guan-Zhi],
Cao, R.[Ryan],
Zhu, Y.[Yuke],
Niebles, J.C.[Juan Carlos],
Fei-Fei, L.[Li],
RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition,
ECCV20(XIX:505-521).
Springer DOI
2011
BibRef
Luo, Z.[Zelun],
Hsieh, J.T.[Jun-Ting],
Jiang, L.[Lu],
Niebles, J.C.[Juan Carlos],
Fei-Fei, L.[Li],
Graph Distillation for Action Detection with Privileged Modalities,
ECCV18(XIV: 174-192).
Springer DOI
1810
BibRef
Huang, D.A.[De-An],
Fei-Fei, L.[Li],
Niebles, J.C.[Juan Carlos],
Connectionist Temporal Modeling for Weakly Supervised Action Labeling,
ECCV16(IV: 137-153).
Springer DOI
1611
BibRef
Niebles, J.C.[Juan Carlos],
Fei-Fei, L.[Li],
A Hierarchical Model of Shape and Appearance for Human Action
Classification,
CVPR07(1-8).
IEEE DOI
0706
BibRef
Niebles, J.C.[Juan Carlos],
Chen, C.W.[Chih-Wei],
Fei-Fei, L.[Li],
Modeling Temporal Structure of Decomposable Motion Segments for
Activity Classification,
ECCV10(II: 392-405).
Springer DOI
1009
See also Olympic Sports Dataset.
BibRef
Lillo, I.[Ivan],
Soto, A.[Alvaro],
Niebles, J.C.[Juan Carlos],
Discriminative Hierarchical Modeling of Spatio-temporally Composable
Human Activities,
CVPR14(812-819)
IEEE DOI
1409
action classification; composable actions; hierarchical modelling
BibRef
Savarese, S.[Silvio],
del Pozo, A.[Andrey],
Niebles, J.C.[Juan Carlos],
Fei-Fei, L.[Li],
Spatial-Temporal correlatons for unsupervised action classification,
Motion08(1-8).
IEEE DOI
0801
BibRef
Ning, H.,
Han, T.X.,
Walther, D.B.,
Liu, M.,
Huang, T.S.,
Hierarchical Space-Time Model Enabling Efficient Search for Human
Actions,
CirSysVideo(19), No. 6, June 2009, pp. 808-820.
IEEE DOI
0906
BibRef
Liu, H.W.[Hao-Wei],
Feris, R.S.[Rogerio S.],
Krueger, V.[Volker],
Sun, M.T.[Ming-Ting],
Unsupervised Action Classification Using Space-Time Link Analysis,
JIVP(2010), No. 2010, pp. xx-yy.
DOI Link
1003
BibRef
Ji, R.R.[Rong-Rong],
Yao, H.X.[Hong-Xun],
Sun, X.S.[Xiao-Shuai],
Actor-independent action search using spatiotemporal vocabulary with
appearance hashing,
PR(44), No. 3, March 2011, pp. 624-638.
Elsevier DOI
1011
Video search; Action retrieval; Attention Shift; 3D-SIFT;
Spatiotemporal vocabulary; Dynamic time warping; Appearance hashing
BibRef
Chakraborty, B.[Bhaskar],
Holte, M.B.[Michael B.],
Moeslund, T.B.[Thomas B.],
Gonzŕlez, J.[Jordi],
Selective spatio-temporal interest points,
CVIU(116), No. 3, March 2012, pp. 396-410.
Elsevier DOI
1201
Action recognition; Complex scenes; Multiple actors; Spatio-temporal
interest points; Local descriptors; Bag-of-words; Support vector
machines
BibRef
Chakraborty, B.[Bhaskar],
Holte, M.B.[Michael B.],
Moeslund, T.B.[Thomas B.],
Gonzalez, J.[Jordi],
Roca, F.X.[F. Xavier],
A selective spatio-temporal interest point detector for human action
recognition in complex scenes,
ICCV11(1776-1783).
IEEE DOI
1201
BibRef
Wang, T.Q.[Tai-Qing],
Wang, S.J.[Sheng-Jin],
Ding, X.Q.[Xiao-Qing],
Detecting Human Action as the Spatio-Temporal Tube of Maximum Mutual
Information,
CirSysVideo(24), No. 2, February 2014, pp. 277-290.
IEEE DOI
1403
Markov processes
BibRef
Gu, J.X.[Jun-Xia],
Ding, X.Q.[Xiao-Qing],
Wang, S.J.[Sheng-Jin],
Wu, Y.S.[You-Shou],
Full body tracking-based human action recognition,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Earlier:
Adaptive particle filter with body part segmentation for full body
tracking,
FG08(1-6).
IEEE DOI
0809
BibRef
Venkataraman, V.[Vinay],
Turaga, P.K.[Pavan K.],
Shape Distributions of Nonlinear Dynamical Systems for Video-Based
Inference,
PAMI(38), No. 12, December 2016, pp. 2531-2543.
IEEE DOI
1609
Analytical models
BibRef
Wang, Q.[Qiao],
Potaraju, C.,
Turaga, P.K.[Pavan K.],
Measuring Glide-Reflection Symmetry in Human Movements Using Elastic
Shape Analysis,
Diff-CVML17(709-716)
IEEE DOI
1709
Foot, Legged locomotion,
Real-time systems, Shape, Trajectory
BibRef
Wang, Q.[Qiao],
Anirudh, R.[Rushil],
Turaga, P.K.[Pavan K.],
Temporal Reflection Symmetry of Human Actions: A Riemannian Analysis,
DIFF-CV15(xx-yy).
DOI Link
1601
BibRef
Bagheri, M.A.[Mohammad Ali],
Gao, Q.G.[Qi-Gang],
Escalera, S.[Sergio],
Moeslund, T.B.[Thomas B.],
Ren, H.M.[Hua-Min],
Etemad, E.[Elham],
Locality regularized group sparse coding for action recognition,
CVIU(158), No. 1, 2017, pp. 106-114.
Elsevier DOI
1704
Bag of words
BibRef
Ren, H.M.[Hua-Min],
Kanhabua, N.[Nattiya],
Mřgelmose, A.[Andreas],
Liu, W.F.[Wei-Feng],
Kulkarni, K.[Kaustubh],
Escalera, S.[Sergio],
Baró, X.[Xavier],
Moeslund, T.B.[Thomas B.],
Back-dropout transfer learning for action recognition,
IET-CV(12), No. 4, June 2018, pp. 484-491.
DOI Link
1805
BibRef
Bagheri, M.A.[Mohammad Ali],
Gao, Q.G.[Qi-Gang],
Escalera, S.[Sergio],
Clapes, A.[Albert],
Nasrollahi, K.[Kamal],
Holte, M.B.[Michael B.],
Moeslund, T.B.[Thomas B.],
Keep it accurate and diverse:
Enhancing action recognition performance by ensemble learning,
ChaLearn15(22-29)
IEEE DOI
1510
Accuracy
BibRef
Lakhal, M.I.[Mohamed Ilyes],
Clapés, A.[Albert],
Escalera, S.[Sergio],
Lanz, O.[Oswald],
Cavallaro, A.[Andrea],
Residual Stacked RNNs for Action Recognition,
HBU18(II:534-548).
Springer DOI
1905
See also Recurrent neural networks for remote sensing image classification.
BibRef
Tseng, C.C.[Chien-Chung],
Chen, J.C.[Ju-Chin],
Fang, C.H.[Ching-Hsien],
Lien, J.J.J.[Jenn-Jier James],
Human action recognition based on graph-embedded spatio-temporal
subspace,
PR(45), No. 10, October 2012, pp. 3611-3624.
Elsevier DOI
1206
BibRef
Earlier: A3, A2, A1, A4:
Human Action Recognition Using Spatio-temporal Classification,
ACCV09(II: 98-109).
Springer DOI
0909
Human action recognition; Adaptive locality preserving projection;
Large margin nearest neighbor
BibRef
Gaidon, A.[Adrien],
Harchaoui, Z.[Zaid],
Schmid, C.[Cordelia],
Temporal Localization of Actions with Actoms,
PAMI(35), No. 11, 2013, pp. 2782-2795.
IEEE DOI
1309
BibRef
Earlier:
Recognizing activities with cluster-trees of tracklets,
BMVC12(30).
DOI Link
1301
BibRef
Earlier:
A time series kernel for action recognition,
BMVC11(xx-yy).
HTML Version.
1110
BibRef
And:
Actom sequence models for efficient action detection,
CVPR11(3201-3208).
IEEE DOI
1106
Action recognition;actoms;temporal localization;video analysis
BibRef
Gaidon, A.[Adrien],
Harchaoui, Z.[Zaid],
Schmid, C.[Cordelia],
Activity representation with motion hierarchies,
IJCV(107), No. 3, May 2014, pp. 219-238.
Springer DOI
1404
Complex activities, example: pole vault.
BibRef
Gaidon, A.[Adrien],
Marszalek, M.[Marcin],
Schmid, C.[Cordelia],
Mining visual actions from movies,
BMVC09(xx-yy).
PDF File.
0909
BibRef
Weinzaepfel, P.,
Harchaoui, Z.[Zaid],
Schmid, C.[Cordelia],
Learning to Track for Spatio-Temporal Action Localization,
ICCV15(3164-3172)
IEEE DOI
1602
Detectors
BibRef
Laptev, I.[Ivan],
Marszalek, M.[Marcin],
Schmid, C.[Cordelia],
Rozenfeld, B.[Benjamin],
Learning realistic human actions from movies,
CVPR08(1-8).
IEEE DOI
0806
See also Structured Learning of Human Interactions in TV Shows.
BibRef
Laptev, I.[Ivan],
Perez, P.[Patrick],
Retrieving actions in movies,
ICCV07(1-8).
IEEE DOI
0710
BibRef
Kviatkovsky, I.[Igor],
Rivlin, E.[Ehud],
Shimshoni, I.[Ilan],
Online action recognition using covariance of shape and motion,
CVIU(129), No. 1, 2014, pp. 15-26.
Elsevier DOI
1411
Online action recognition
BibRef
Derpanis, K.G.P.[Konstantinos G.P.],
Sizintsev, M.[Mikhail],
Cannons, K.[Kevin],
Wildes, R.P.[Richard P.],
Action Spotting and Recognition Based on a Spatiotemporal Orientation
Analysis,
PAMI(35), No. 3, March 2013, pp. 527-540.
IEEE DOI
1303
BibRef
Earlier:
Efficient action spotting based on a spacetime oriented structure
representation,
CVPR10(1990-1997).
IEEE DOI
1006
Combine action spotting, action recognition, classification into
category.
human actions in video. Descriptors computed from raw intensity data.
See also Spatiotemporal Stereo and Scene Flow via Stequel Matching.
BibRef
Zhang, W.Y.[Wei-Yu],
Zhu, M.L.[Meng-Long],
Derpanis, K.G.P.[Konstantinos G.P.],
From Actemes to Action: A Strongly-Supervised Representation for
Detailed Action Understanding,
ICCV13(2248-2255)
IEEE DOI
1403
action classification, action detection
See also Penn Action Dataset.
BibRef
Sizintsev, M.[Mikhail],
Wildes, R.P.[Richard P.],
Spatiotemporal oriented energies for spacetime stereo,
ICCV11(1140-1147).
IEEE DOI
1201
BibRef
Ma, A.J.H.[Andy Jin-Hua],
Yuen, P.C.[Pong C.],
Zou, W.W.W.[Wilman Wei-Wen],
Lai, J.H.[Jian-Huang],
Supervised Spatio-Temporal Neighborhood Topology Learning for Action
Recognition,
CirSysVideo(23), No. 8, 2013, pp. 1447-1460.
IEEE DOI
1307
BibRef
Earlier:
Supervised Neighborhood Topology Learning for Human Action Recognition,
MLMotion09(476-481).
IEEE DOI
0910
BibRef
Zhang, X.R.[Xiang-Rong],
Yang, Y.[Yang],
Jiao, L.C.[Li-Cheng],
Dong, F.[Feng],
Manifold-constrained coding and sparse representation for human action
recognition,
PR(46), No. 7, July 2013, pp. 1819-1831.
Elsevier DOI
1303
Human action recognition; Local manifold-constrained coding; Sparse
representation; Bag-of-features model; Spatio-temporal local features
BibRef
Zhang, X.R.[Xiang-Rong],
Yang, H.[Hao],
Jiao, L.C.,
Yang, Y.[Yang],
Dong, F.[Feng],
Laplacian group sparse modeling of human actions,
PR(47), No. 8, 2014, pp. 2689-2701.
Elsevier DOI
1405
Action recognition
BibRef
Ahmed, J.[Javed],
Abbasi, S.[Sadaf],
Shaikh, M.Z.[M. Zakir],
Fast spatiotemporal MACH filter for action recognition,
MVA(24), No. 5, July 2013, pp. 909-918.
WWW Link.
1306
BibRef
Burghouts, G.J.[Gertjan J.],
Schutte, K.[Klamer],
Spatio-temporal layout of human actions for improved bag-of-words
action detection,
PRL(34), No. 15, 2013, pp. 1861-1869.
Elsevier DOI
1309
BibRef
Earlier:
Correlations between 48 human actions improve their detection,
ICPR12(3815-3818).
WWW Link.
1302
Human action recognition
See also unified approach to the recognition of complex actions from sequences of zone-crossings, A.
BibRef
Burghouts, G.J.[Gertjan J.],
Eendebak, P.[Pieter],
Bouma, H.[Henri],
ten Hove, R. .J.M.[R. Johan-Martijn],
Improved action recognition by combining multiple 2D views in the
bag-of-words model,
AVSS13(250-255)
IEEE DOI
1311
Accuracy
BibRef
Burghouts, G.J.,
van den Broek, S.P.,
ten Hove, R.J.M.,
Spatio-temporal Saliency for Action Similarity,
ActionSim13(257-262)
IEEE DOI
1309
Saliency map
BibRef
Borzeshi, E.Z.[E. Zare],
Perez Concha, O.[Oscar],
Xu, R.Y.D.[Richard Yi Da],
Piccardi, M.[Massimo],
Joint Action Segmentation and Classification by an Extended Hidden
Markov Model,
SPLetters(20), No. 12, 2013, pp. 1207-1210.
IEEE DOI
1311
Accuracy
BibRef
Borzeshi, E.Z.[Ehsan Zare],
Perez Concha, O.[Oscar],
Piccardi, M.[Massimo],
Human Action Recognition in Video by Fusion of Structural and
Spatio-temporal Features,
SSSPR12(474-482).
Springer DOI
1211
BibRef
Borzeshi, E.Z.[Ehsan Zare],
Xu, R.Y.D.[Richard Yi Da],
Piccardi, M.[Massimo],
Automatic Human Action Recognition in Videos by Graph Embedding,
CIAP11(II: 19-28).
Springer DOI
1109
BibRef
Perez Concha, O.[Oscar],
Xu, R.Y.D.[Richard Yi Da],
Piccardi, M.[Massimo],
Compressive Sensing of Time Series for Human Action Recognition,
DICTA10(454-461).
IEEE DOI
1012
BibRef
Cheng, J.[Jian],
Liu, H.J.[Hai-Jun],
Li, H.S.[Hong-Sheng],
Silhouette analysis for human action recognition based on maximum
spatio-temporal dissimilarity embedding,
MVA(25), No. 4, May 2014, pp. 1007-1018.
WWW Link.
1404
BibRef
Talha, A.M.[Ayesha M.],
Junejo, I.N.[Imran N.],
Dynamic scene understanding using temporal association rules,
IVC(32), No. 12, 2014, pp. 1102-1116.
Elsevier DOI
1412
Scene understanding. Spatio-temporal abnormalities in event analysis.
BibRef
Emami, A.[Ali],
Harandi, M.T.[Mehrtash T.],
Dadgostar, F.[Farhad],
Lovell, B.C.[Brian C.],
Novelty detection in human tracking based on spatiotemporal oriented
energies,
PR(48), No. 3, 2015, pp. 812-826.
Elsevier DOI
1412
Occlusion modeling
BibRef
Nguyen, T.V.,
Song, Z.[Zheng],
Yan, S.C.[Shui-Cheng],
STAP: Spatial-Temporal Attention-Aware Pooling for Action Recognition,
CirSysVideo(25), No. 1, January 2015, pp. 77-86.
IEEE DOI
1502
gesture recognition
BibRef
Ding, W.W.[Wen-Wen],
Liu, K.[Kai],
Cheng, F.[Fei],
Zhang, J.[Jin],
STFC: Spatio-Temporal Feature Chain for Skeleton-Based Human Action
Recognition,
JVCIR(26), No. 1, 2015, pp. 329-337.
Elsevier DOI
1502
View-invariant representation
BibRef
Ding, W.W.[Wen-Wen],
Liu, K.[Kai],
Belyaev, E.[Evgeny],
Cheng, F.[Fei],
Tensor-based linear dynamical systems for action recognition from 3D
skeletons,
PR(77), 2018, pp. 75-86.
Elsevier DOI
1802
Skeleton joints, Action recognition, Subspace learning,
Tensor learning, Grassmann manifold
BibRef
Ding, W.W.[Wen-Wen],
Liu, K.[Kai],
Cheng, F.[Fei],
Zhang, J.[Jin],
Learning hierarchical spatio-temporal pattern for human activity
prediction,
JVCIR(35), No. 1, 2016, pp. 103-111.
Elsevier DOI
1602
Skeleton joints
BibRef
Ding, W.W.[Wen-Wen],
Liu, K.[Kai],
Fu, X.[Xujia],
Cheng, F.[Fei],
Profile HMMs for skeleton-based human action recognition,
SP:IC(42), No. 1, 2016, pp. 109-119.
Elsevier DOI
1603
View-invariant representation
BibRef
Li, Y.[Yang],
Ye, J.Y.[Jun-Yong],
Wang, T.Q.[Tong-Qing],
Huang, S.J.[Shi-Jian],
Augmenting bag-of-words: a robust contextual representation of
spatiotemporal interest points for action recognition,
VC(31), No. 10, October 2015, pp. 1383-1394.
WWW Link.
1509
BibRef
Li, Y.[Yang],
Ye, J.Y.[Jun-Yong],
Wang, T.Q.[Tong-Qing],
Huang, S.J.[Shi-Jian],
Statistics on Temporal Changes of Sparse Coding Coefficients in Spatial
Pyramids for Human Action Recognition,
IEICE(E98-D), No. 9, September 2015, pp. 1711-1714.
WWW Link.
1509
BibRef
Huang, S.J.[Shi-Jian],
Ye, J.Y.[Jun-Yong],
Wang, T.Q.[Tong-Qing],
Jiang, L.[Li],
Xing, C.Y.[Chang-Yuan],
Li, Y.[Yang],
Learning a Similarity Constrained Discriminative Kernel Dictionary from
Concatenated Low-Rank Features for Action Recognition,
IEICE(E99-D), No. 2, February 2016, pp. 541-544.
WWW Link.
1604
BibRef
Kihl, O.[Olivier],
Picard, D.[David],
Gosselin, P.H.[Philippe-Henri],
A unified framework for local visual descriptors evaluation,
PR(48), No. 4, 2015, pp. 1174-1184.
Elsevier DOI
1502
BibRef
Earlier:
A unified formalism for video descriptors,
ICIP13(2416-2419)
IEEE DOI
1402
Image processing and computer vision.
action analysis
BibRef
Kihl, O.[Olivier],
Picard, D.[David],
Gosselin, P.H.[Philippe-Henri],
Local polynomial space-time descriptors for action classification,
MVA(27), No. 3, April 2016, pp. 351-361.
WWW Link.
1604
BibRef
Pei, L.S.[Li-Shen],
Ye, M.[Mao],
Zhao, X.Z.[Xue-Zhuan],
Xiang, T.[Tao],
Li, T.[Tao],
Learning spatio-temporal features for action recognition from the side
of the video,
SIViP(10), No. 1, January 2016, pp. 199-206.
WWW Link.
1601
BibRef
Dawn, D.D.[Debapratim Das],
Shaikh, S.H.[Soharab Hossain],
A comprehensive survey of human action recognition with spatio-temporal
interest point (STIP) detector,
VC(32), No. 3, March 2016, pp. 289-306.
WWW Link.
1604
BibRef
Tran, D.[Du],
Torresani, L.[Lorenzo],
EXMOVES: Mid-level Features for Efficient Action Recognition and Video
Analysis,
IJCV(119), No. 3, September 2016, pp. 239-253.
Springer DOI
1608
BibRef
Tran, D.[Du],
Bourdev, L.[Lubomir],
Fergus, R.[Rob],
Torresani, L.[Lorenzo],
Paluri, M.[Manohar],
Learning Spatiotemporal Features with 3D Convolutional Networks,
ICCV15(4489-4497)
IEEE DOI
1602
3D CNN,
Convolution
BibRef
Korbar, B.,
Tran, D.[Du],
Torresani, L.[Lorenzo],
SCSampler: Sampling Salient Clips From Video for Efficient Action
Recognition,
ICCV19(6231-6241)
IEEE DOI
2004
feature extraction, image classification,
image motion analysis, learning (artificial intelligence),
BibRef
Liu, Y.N.[Yi-Nan],
Wu, Q.B.[Qing-Bo],
Xu, L.F.[Lin-Feng],
Wu, B.[Bo],
Mining Spatial Temporal Saliency Structure for Action Recognition,
IEICE(E99-D), No. 10, October 2016, pp. 2643-2646.
WWW Link.
1610
BibRef
Liu, Y.N.[Yi-Nan],
Wu, Q.B.[Qing-Bo],
Tang, L.Z.[Liang-Zhi],
Xu, L.F.[Lin-Feng],
Self-Supervised Learning of Video Representation for Anticipating
Actions in Early Stage,
IEICE(E101-D), No. 5, May 2018, pp. 1449-1452.
WWW Link.
1805
BibRef
Megrhi, S.[Sameh],
Jmal, M.[Marwa],
Souidene, W.[Wided],
Beghdadi, A.[Azeddine],
Spatio-temporal action localization and detection for human action
recognition in big dataset,
JVCIR(41), No. 1, 2016, pp. 375-390.
Elsevier DOI
1612
Spatio-temporal action detection
BibRef
Yang, X.D.[Xiao-Dong],
Tian, Y.L.[Ying-Li],
Super Normal Vector for Human Activity Recognition with Depth Cameras,
PAMI(39), No. 5, May 2017, pp. 1028-1039.
IEEE DOI
1704
BibRef
Earlier:
Super Normal Vector for Activity Recognition Using Depth Sequences,
CVPR14(804-811)
IEEE DOI
1409
BibRef
And:
Action Recognition Using Super Sparse Coding Vector with
Spatio-temporal Awareness,
ECCV14(II: 727-741).
Springer DOI
1408
Cameras
BibRef
Ulhaq, A.[Anwaar],
Yin, X.X.S.[Xiao-Xia Sunny],
He, J.[Jing],
Zhang, Y.C.[Yan-Chun],
On Space-Time Filtering Framework for Matching Human Actions Across
Different Viewpoints,
IP(27), No. 3, March 2018, pp. 1230-1242.
IEEE DOI
1801
Correlation, Fourier transforms, Frequency-domain analysis,
Image recognition, Tensile stress, view-invariance
BibRef
Nazir, S.[Saima],
Yousaf, M.H.[Muhammad Haroon],
Nebel, J.C.[Jean-Christophe],
Velastin, S.A.[Sergio A.],
A Bag of Expression framework for improved human action recognition,
PRL(103), 2018, pp. 39-45.
Elsevier DOI
1802
BibRef
Earlier: A1, A2, A4, Only:
Feature Similarity and Frequency-Based Weighted Visual Words Codebook
Learning Scheme for Human Action Recognition,
PSIVT17(326-336).
Springer DOI
1802
human action in simple and realistic scenarios. Add space-time to BoW.
Human action recognition, Bag of Words, Bag of visual words
BibRef
Murtaza, F.[Fiza],
Yousaf, M.H.[Muhammad Haroon],
Velastin, S.A.[Sergio A.],
TAB: Temporally aggregated bag-of-discriminant-words for temporal
action proposals,
CVIU(183), 2019, pp. 42-52.
Elsevier DOI
1906
Temporal action detection, Bag of words, temporal action proposals
BibRef
Murtaza, F.[Fiza],
Yousaf, M.H.[Muhammad Haroon],
Velastin, S.A.[Sergio A.],
Qian, Y.,
End-to-End Temporal Action Detection Using Bag of Discriminant
Snippets,
SPLetters(26), No. 2, February 2019, pp. 272-276.
IEEE DOI
1902
feature extraction, gesture recognition, image classification,
image motion analysis, image representation,
temporal-action proposals
BibRef
Naeem, H.B.[Hajra Binte],
Murtaza, F.[Fiza],
Yousaf, M.H.[Muhammad Haroon],
Velastin, S.A.[Sergio A.],
T-VLAD: Temporal vector of locally aggregated descriptor for
multiview human action recognition,
PRL(148), 2021, pp. 22-28.
Elsevier DOI
2107
BibRef
Earlier: A2, A3, A4, Only:
DA-VLAD: Discriminative Action Vector of Locally Aggregated
Descriptors for Action Recognition,
ICIP18(3993-3997)
IEEE DOI
1809
Human action recognition, Multi-view, View-invariant,
Temporal action sequence, VLAD, Short segment features.
Feature extraction, Encoding, Videos, Task analysis, Training,
Trajectory, Standards, Human action recognition,
improved dense trajectories (iDT)
BibRef
Xu, W.[Wanru],
Miao, Z.J.[Zhen-Jiang],
Zhang, X.P.,
Tian, Y.[Yi],
A Hierarchical Spatio-Temporal Model for Human Activity Recognition,
MultMed(19), No. 7, July 2017, pp. 1494-1509.
IEEE DOI
1706
Activity recognition, Computational modeling, Feature extraction,
Hidden Markov models, Multimedia communication, Streaming media,
Video sequences, Activity recognition,
hidden conditional random field (HCRF), hierarchical structure,
spatio-temporal, dependencies
BibRef
Tian, Y.[Yi],
Kong, Y.[Yu],
Ruan, Q.Q.[Qiu-Qi],
An, G.Y.[Gao-Yun],
Fu, Y.[Yun],
Hierarchical and Spatio-Temporal Sparse Representation for Human
Action Recognition,
IP(27), No. 4, April 2018, pp. 1748-1762.
IEEE DOI
1802
Correlation, Encoding, Hidden Markov models, Image coding, Layout,
Video sequences, Visualization, Action Recognition,
locally consistent group sparse coding
BibRef
Xu, W.[Wanru],
Miao, Z.J.[Zhen-Jiang],
Zhang, J.[Jian],
Tian, Y.[Yi],
Learning Spatio-Temporal Features for Action Recognition with Modified
Hidden Conditional Random Field,
VECTaR14(786-801).
Springer DOI
1504
BibRef
Xu, W.[Wanru],
Miao, Z.J.[Zhen-Jiang],
Zhang, J.[Jian],
Zhang, Q.A.[Qi-Ang],
Wu, H.[Hao],
Spatial-Temporal Context for Action Recognition Combined with
Confidence and Contribution Weight,
ACPR13(576-580)
IEEE DOI
1408
data mining
BibRef
Martínez, F.[Fabio],
Manzanera, A.[Antoine],
Romero, E.[Eduardo],
Spatio-temporal multi-scale motion descriptor from a
spatially-constrained decomposition for online action recognition,
IET-CV(11), No. 7, October 2017, pp. 541-549.
DOI Link
1709
BibRef
Jia, C.,
Shao, M.,
Li, S.,
Zhao, H.,
Fu, Y.,
Stacked Denoising Tensor Auto-Encoder for Action Recognition With
Spatiotemporal Corruptions,
IP(27), No. 4, April 2018, pp. 1878-1887.
IEEE DOI
1802
correlation methods, divide and conquer methods,
feature extraction, image denoising, image motion analysis,
spatiotemporal corruption
BibRef
Ma, S.[Shugao],
Zhang, J.M.[Jian-Ming],
Sclaroff, S.[Stan],
Ikizler-Cinbis, N.[Nazli],
Sigal, L.[Leonid],
Space-Time Tree Ensemble for Action Recognition and Localization,
IJCV(126), No. 2-4, April 2018, pp. 314-332.
Springer DOI
1804
BibRef
Earlier: A1, A2, A4, A3, Only:
Action Recognition and Localization by Hierarchical Space-Time
Segments,
ICCV13(2744-2751)
IEEE DOI
1403
action localization; action recognition; space-time representation
BibRef
Ma, S.[Shugao],
Sigal, L.[Leonid],
Sclaroff, S.[Stan],
Learning Activity Progression in LSTMs for Activity Detection and
Early Detection,
CVPR16(1942-1950)
IEEE DOI
1612
BibRef
Earlier:
Space-time tree ensemble for action recognition,
CVPR15(5024-5032)
IEEE DOI
1510
BibRef
Li, Y.S.[Yan-Shan],
Xia, R.J.[Rong-Jie],
Xie, W.X.[Wei-Xin],
A unified model of appearance and motion of video and its application
in STIP detection,
SIViP(12), No. 3, March 2018, pp. 403-410.
Springer DOI
1804
Spatio-temporal interest points for action recognition.
BibRef
Yu, T.Z.[Ting-Zhao],
Guo, C.X.[Chao-Xu],
Wang, L.F.[Ling-Feng],
Gu, H.X.[Hu-Xiang],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Joint spatial-temporal attention for action recognition,
PRL(112), 2018, pp. 226-233.
Elsevier DOI
1809
BibRef
Earlier: A1, A4, A3, A5, A6, Only:
Cascaded temporal spatial features for video action recognition,
ICIP17(1552-1556)
IEEE DOI
1803
Action recognition, Spatial-Temporal attention, Two-Stage.
Convolution, Feature extraction,
Training, spatial-temporal decomposition
BibRef
Yu, T.Z.[Ting-Zhao],
Wang, L.F.[Ling-Feng],
Guo, C.X.[Chao-Xu],
Gu, H.X.[Hu-Xiang],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Pseudo low rank video representation,
PR(85), 2019, pp. 50-59.
Elsevier DOI
1810
Pseudo low rank, Data driven, Low resolution, Action recognition
BibRef
Song, L.F.[Li-Fei],
Weng, L.G.[Li-Guo],
Wang, L.F.[Ling-Feng],
Min, X.[Xia],
Pan, C.H.[Chun-Hong],
Two-Stream Designed 2D/3D Residual Networks with LSTMS for Action
Recognition in Videos,
ICIP18(808-812)
IEEE DOI
1809
Videos, Solid modeling,
Convolution, Logic gates, Training,
score distribution fusion
BibRef
Bhorge, S.B.[Sidharth B.],
Manthalkar, R.R.[Ramachandra R.],
Three-dimensional spatio-temporal trajectory descriptor for human
action recognition,
MultInfoRetr(8), No. 3, September 2018, pp. 197-205.
Springer DOI
1809
BibRef
Tong, M.[Ming],
Chen, Y.R.[Yi-Ran],
Zhao, M.G.[Men-Gao],
Tian, W.J.[Wei-Juan],
A new framework of action recognition with discriminative parts,
spatio-temporal and causal interaction descriptors,
JVCIR(56), 2018, pp. 116-130.
Elsevier DOI
1811
Action recognition, Spectral clustering,
Discriminative constraint, Action part, Causal relationship
BibRef
Tu, Z.G.[Zhi-Gang],
Li, H.Y.[Hong-Yan],
Zhang, D.J.[De-Jun],
Dauwels, J.[Justin],
Li, B.X.[Bao-Xin],
Yuan, J.S.[Jun-Song],
Action-Stage Emphasized Spatiotemporal VLAD for Video Action
Recognition,
IP(28), No. 6, June 2019, pp. 2799-2812.
IEEE DOI
1905
feature extraction, gesture recognition,
image colour analysis, image motion analysis,
ActionS-ST-VLAD
BibRef
Abrishami-Moghaddam, H.[Hamid],
Zare, A.[Amin],
Spatiotemporal wavelet correlogram for human action recognition,
MultInfoRetr(8), No. 3, September 2019, pp. 167-180.
WWW Link.
1908
BibRef
Xue, F.[Fei],
Ji, H.B.[Hong-Bing],
Zhang, W.B.[Wen-Bo],
Cao, Y.[Yi],
Attention-based spatial-temporal hierarchical ConvLSTM network for
action recognition in videos,
IET-CV(13), No. 8, December 2019, pp. 708-718.
DOI Link
1912
BibRef
Xue, F.[Fei],
Ji, H.B.[Hong-Bing],
Zhang, W.B.[Wen-Bo],
Cao, Y.[Yi],
Self-supervised video representation learning by maximizing mutual
information,
SP:IC(88), 2020, pp. 115967.
Elsevier DOI
2009
Different clips from same video share some features.
Self-supervised learning, Deep learning, Video representation,
Mutual information, Action recognition
BibRef
Xue, F.[Fei],
Ji, H.B.[Hong-Bing],
Zhang, W.B.[Wen-Bo],
Mutual information guided 3D ResNet for self-supervised video
representation learning,
IET-IPR(14), No. 13, November 2020, pp. 3066-3075.
DOI Link
2012
BibRef
Ye, Y.C.[Yuan-Cheng],
Yang, X.D.[Xiao-Dong],
Tian, Y.L.[Ying-Li],
Discovering spatio-temporal action tubes,
JVCIR(58), 2019, pp. 515-524.
Elsevier DOI
1901
Spatio-temporal action detection, Deep neural networks
BibRef
Jing, L.L.[Long-Long],
Ye, Y.C.[Yuan-Cheng],
Yang, X.D.[Xiao-Dong],
Tian, Y.L.[Ying-Li],
3D convolutional neural network with multi-model framework for action
recognition,
ICIP17(1837-1841)
IEEE DOI
1803
Data mining, Feature extraction, Optical computing,
Optical fiber networks, Optical flow,
Video Classification
BibRef
Song, S.,
Lan, C.L.[Cui-Ling],
Xing, J.L.[Jun-Liang],
Zeng, W.J.[Wen-Jun],
Liu, J.Y.[Jia-Ying],
Spatio-Temporal Attention-Based LSTM Networks for 3D Action
Recognition and Detection,
IP(27), No. 7, July 2018, pp. 3459-3471.
IEEE DOI
1805
Computational modeling, Feature extraction, Proposals,
Recurrent neural networks, Skeleton, temporal attention
BibRef
Zhou, Y.Z.[Yi-Zhou],
Sun, X.Y.[Xiao-Yan],
Zha, Z.J.[Zheng-Jun],
Zeng, W.J.[Wen-Jun],
MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition,
CVPR18(449-458)
IEEE DOI
1812
3D CNN is high complexity training, integrate 2D CNN to get 3D feature maps.
Convolution, Kernel, Videos, Image recognition, Training
BibRef
Li, Y.H.[Yang-Hao],
Lan, C.L.[Cui-Ling],
Xing, J.L.[Jun-Liang],
Zeng, W.J.[Wen-Jun],
Yuan, C.F.[Chun-Feng],
Liu, J.Y.[Jia-Ying],
Online Human Action Detection Using Joint Classification-Regression
Recurrent Neural Networks,
ECCV16(VII: 203-220).
Springer DOI
1611
BibRef
Soltanian, M.,
Amini, S.,
Ghaemmaghami, S.,
Spatio-Temporal VLAD Encoding of Visual Events Using Temporal
Ordering of the Mid-Level Deep Semantics,
MultMed(22), No. 7, July 2020, pp. 1769-1784.
IEEE DOI
2007
Encoding, Visualization, Semantics, Task analysis, Convex functions,
Principal component analysis, Training,
support vector machine
BibRef
Li, D.[Dong],
Yao, T.[Ting],
Duan, L.Y.[Ling-Yu],
Mei, T.[Tao],
Rui, Y.[Yong],
Unified Spatio-Temporal Attention Networks for Action Recognition in
Videos,
MultMed(21), No. 2, February 2019, pp. 416-428.
IEEE DOI
1902
Videos, Feature extraction,
Task analysis,
deep convolutional networks
BibRef
Li, T.J.[Tian-Jiao],
Luo, Y.[Yang],
Zhang, W.[Wei],
Duan, L.Y.[Ling-Yu],
Liu, J.[Jun],
HARDer-Net: Hardness-Guided Discrimination Network for 3D Early
Activity Prediction,
CirSysVideo(34), No. 12, December 2024, pp. 12112-12126.
IEEE DOI
2501
BibRef
Earlier: A1, A5, A3, A4, Only:
Hard-Net: Hardness-aware Discrimination Network for 3d Early Activity
Prediction,
ECCV20(XI:420-436).
Springer DOI
2011
Predictive models, Interference,
Adversarial machine learning, Solid modeling, Task analysis,
hardness-guided learning
BibRef
Li, D.[Dong],
Qiu, Z.F.[Zhao-Fan],
Pan, Y.W.[Ying-Wei],
Yao, T.[Ting],
Li, H.Q.[Hou-Qiang],
Mei, T.[Tao],
Representing Videos as Discriminative Sub-graphs for Action
Recognition,
CVPR21(3309-3318)
IEEE DOI
2111
Prototypes,
Complexity theory, Proposals, Kernel
BibRef
Li, D.[Dong],
Qiu, Z.F.[Zhao-Fan],
Dai, Q.[Qi],
Yao, T.[Ting],
Mei, T.[Tao],
Recurrent Tubelet Proposal and Recognition Networks for Action
Detection,
ECCV18(VI: 306-322).
Springer DOI
1810
BibRef
Hao, W.L.[Wang-Li],
Zhang, Z.X.[Zhao-Xiang],
Spatiotemporal distilled dense-connectivity network for video action
recognition,
PR(92), 2019, pp. 13-24.
Elsevier DOI
1905
Two-stream, Action recognition, Dense-connectivity, Knowledge distillation
BibRef
Escorcia, V.[Victor],
Dao, C.D.[Cuong D.],
Jain, M.[Mihir],
Ghanem, B.[Bernard],
Snoek, C.G.M.[Cees G.M.],
Guess where? Actor-supervision for spatiotemporal action localization,
CVIU(192), 2020, pp. 102886.
Elsevier DOI
2002
Actor-supervision, Spatiotemporal action localization,
Action understanding, Video analysis, Weakly-supervised
BibRef
Song, X.,
Lan, C.,
Zeng, W.,
Xing, J.,
Sun, X.,
Yang, J.,
Temporal-Spatial Mapping for Action Recognition,
CirSysVideo(30), No. 3, March 2020, pp. 748-759.
IEEE DOI
2003
Feature extraction, Optical imaging, Computational modeling,
deep learning
BibRef
Zhang, D.J.[De-Jun],
He, L.C.[Lin-Chao],
Tu, Z.G.[Zhi-Gang],
Zhang, S.[Shifu],
Han, F.[Fei],
Yang, B.X.[Bo-Xiong],
Learning motion representation for real-time spatio-temporal action
localization,
PR(103), 2020, pp. 107312.
Elsevier DOI
2005
Spatio-Temporal Action Localization, Real-time Computation,
Optical Flow Sub-network, Pyramid Hierarchical Fusion
BibRef
Liu, Y.Z.[Yuan-Zhong],
Tu, Z.G.[Zhi-Gang],
Lin, L.[Liyu],
Xie, X.[Xing],
Qin, Q.Q.[Qian-Qing],
Real-time Spatio-temporal Action Localization via Learning Motion
Representation,
MMHUA20(184-198).
Springer DOI
2103
BibRef
Yang, H.,
Yuan, C.,
Zhang, L.,
Sun, Y.,
Hu, W.,
Maybank, S.J.,
STA-CNN: Convolutional Spatial-Temporal Attention Learning for Action
Recognition,
IP(29), 2020, pp. 5783-5793.
IEEE DOI
2005
Videos, Feature extraction, Motion segmentation,
Computational modeling, Image recognition, Solid modeling,
action recognition
BibRef
Yu, J.[Jongmin],
Kim, D.Y.[Du Yong],
Yoon, Y.[Yongsang],
Jeon, M.[Moongu],
Action matching network: open-set action recognition using
spatio-temporal representation matching,
VC(36), No. 7, July 2020, pp. 1457-1471.
WWW Link.
2005
BibRef
Baddar, W.J.[Wissam J.],
Ro, Y.M.[Yong Man],
Encoding features robust to unseen modes of variation with attentive
long short-term memory,
PR(100), 2020, pp. 107159.
Elsevier DOI
2005
Long short-term memory, Recurrent neural networks, Attention,
Robust features, Modes of variation, Human action recognition
BibRef
Seo, J.J.[Jeong-Jik],
Baddar, W.J.[Wissam J.],
Kim, D.H.[Dae Hoe],
Ro, Y.M.[Yong Man],
Human action recognition using time-invariant key-trajectories
describing spatio-temporal salient motion,
ICIP15(586-590)
IEEE DOI
1512
Human action recognition
BibRef
Yang, C.,
Xu, Y.,
Shi, J.,
Dai, B.,
Zhou, B.,
Temporal Pyramid Network for Action Recognition,
CVPR20(588-597)
IEEE DOI
2008
Visualization, Semantics, Videos, Modulation,
Feature extraction
BibRef
Huang, J.,
Li, N.,
Li, T.,
Liu, S.,
Li, G.,
Spatial-Temporal Context-Aware Online Action Detection and Prediction,
CirSysVideo(30), No. 8, August 2020, pp. 2650-2662.
IEEE DOI
2008
Videos, Electron tubes, Proposals, Context modeling,
Object detection, Predictive models, Computational modeling,
online action tube generation
BibRef
Jiang, M.[Min],
Pan, N.[Na],
Kong, J.[Jun],
Spatial-temporal saliency action mask attention network for action
recognition,
JVCIR(71), 2020, pp. 102846.
Elsevier DOI
2009
Action recognition, Two-stream, Saliency attention, Key-frame
BibRef
Li, Y.X.[Yu-Xi],
Lin, W.Y.[Wei-Yao],
See, J.[John],
Xu, N.[Ning],
Xu, S.G.[Shu-Gong],
Yan, K.[Ke],
Yang, C.[Cong],
CFAD: Coarse-to-fine Action Detector for Spatiotemporal Action
Localization,
ECCV20(XVI: 510-527).
Springer DOI
2010
BibRef
Cai, J.H.[Jia-Hui],
Hu, J.G.[Jian-Guo],
Li, S.[Shiren],
Lin, J.L.[Jia-Ling],
Wang, J.[Jun],
Combination of temporal-channels correlation information and bilinear
feature for action recognition,
IET-CV(14), No. 8, December 2020, pp. 634-641.
DOI Link
2012
BibRef
Eun, H.[Hyunjun],
Moon, J.Y.[Jin-Young],
Park, J.Y.[Jong-Youl],
Jung, C.[Chanho],
Kim, C.[Changick],
Temporal filtering networks for online action detection,
PR(111), 2021, pp. 107695.
Elsevier DOI
2012
Online action detection, Temporal filtering networks, Filter modules, TFN
BibRef
Tomei, M.[Matteo],
Baraldi, L.[Lorenzo],
Calderara, S.[Simone],
Bronzin, S.[Simone],
Cucchiara, R.[Rita],
Video action detection by learning graph-based spatio-temporal
interactions,
CVIU(206), 2021, pp. 103187.
Elsevier DOI
2104
Video understanding, Action detection, Graph learning
BibRef
Tapaswi, M.[Makarand],
Kumar, V.[Vijay],
Laptev, I.[Ivan],
Long term spatio-temporal modeling for action detection,
CVIU(210), 2021, pp. 103242.
Elsevier DOI
2109
Spatio-temporal action detection, Graph neural networks, Atomic visual actions
BibRef
Cao, H.Z.[Hao-Zhi],
Xu, Y.C.[Yue-Cong],
Yang, J.F.[Jian-Fei],
Mao, K.Z.[Ke-Zhi],
Yin, J.X.[Jian-Xiong],
See, S.[Simon],
Effective action recognition with embedded key point shifts,
PR(120), 2021, pp. 108172.
Elsevier DOI
2109
Action recognition, Temporal feature, Key point shifts
BibRef
Xu, Y.C.[Yue-Cong],
Cao, H.Z.[Hao-Zhi],
Yin, J.X.[Jian-Xiong],
Chen, Z.H.[Zheng-Hua],
Li, X.L.[Xiao-Li],
Li, Z.G.[Zheng-Guo],
Xu, Q.W.[Qian-Wen],
Yang, J.F.[Jian-Fei],
Going Deeper into Recognizing Actions in Dark Environments:
A Comprehensive Benchmark Study,
IJCV(132), No. 4, April 2024, pp. 1292-1309.
Springer DOI
2404
BibRef
Wang, Y.Z.[Yan-Ze],
Ye, J.Y.[Jun-Yong],
TMF: Temporal Motion and Fusion for action recognition,
CVIU(213), 2021, pp. 103304.
Elsevier DOI
2112
Action recognition, Motion extraction, Temporal crossing fusion
BibRef
Fu, H.[Hui],
Zhang, K.[Ke],
Li, H.Y.[Hao-Yu],
Wang, J.Y.[Jing-Yu],
Wang, Z.[Zhen],
Spatial Temporal and Channel Aware Network for Video-Based Person
Re-Identification,
IVC(118), 2022, pp. 104356.
Elsevier DOI
2202
Video-based Re-ID, Spatial temporal feature,
Channel segmentation, Group shuffle convolution
BibRef
Zhu, L.C.[Lin-Chao],
Fan, H.[Hehe],
Luo, Y.[Yawei],
Xu, M.L.[Ming-Liang],
Yang, Y.[Yi],
Temporal Cross-Layer Correlation Mining for Action Recognition,
MultMed(24), 2022, pp. 668-676.
IEEE DOI
2202
Convolution, Logic gates, Correlation, Trajectory, Aggregates,
Training, Deep learning, video feature learning,
frame correlation mining
BibRef
Kushwaha, A.[Arati],
Khare, A.[Ashish],
Khare, M.[Manish],
Human Activity Recognition Algorithm in Video Sequences Based on
Integration of Magnitude and Orientation Information of Optical Flow,
IJIG(22), No. 1 2022, pp. 2250009.
DOI Link
2202
BibRef
Shen, Z.W.[Zhong-Wei],
Wu, X.J.[Xiao-Jun],
Xu, T.Y.[Tian-Yang],
FEXNet: Foreground Extraction Network for Human Action Recognition,
CirSysVideo(32), No. 5, May 2022, pp. 3141-3151.
IEEE DOI
2205
Convolutional neural networks, Spatiotemporal phenomena,
Feature extraction, Solid modeling,
action recognition
BibRef
Wu, C.[Cong],
Wu, X.J.[Xiao-Jun],
Xu, T.Y.[Tian-Yang],
Kittler, J.V.[Josef V.],
Scene adaptive mechanism for action recognition,
CVIU(238), 2024, pp. 103854.
Elsevier DOI
2312
Scene adaptive mechanism, Action recognition
BibRef
Wang, J.P.[Jin-Peng],
Lin, Y.Q.[Yi-Qi],
Zhang, M.L.[Man-Lin],
Gao, Y.[Yuan],
Ma, A.J.[Andy J.],
Multi-Level Temporal Dilated Dense Prediction for Action Recognition,
MultMed(24), 2022, pp. 2553-2566.
IEEE DOI
2205
Feature extraction, Convolution,
Image recognition, Task analysis, Solid modeling,
3D Convolutional Neural Network
BibRef
Wang, J.L.[Jiang-Liu],
Jiao, J.B.[Jian-Bo],
Bao, L.C.[Lin-Chao],
He, S.F.[Sheng-Feng],
Liu, W.[Wei],
Liu, Y.H.[Yun-Hui],
Self-Supervised Video Representation Learning by Uncovering
Spatio-Temporal Statistics,
PAMI(44), No. 7, July 2022, pp. 3791-3806.
IEEE DOI
2206
BibRef
Earlier: A1, A2, A3, A4, A6, A5:
Self-Supervised Spatio-Temporal Representation Learning for Videos by
Predicting Motion and Appearance Statistics,
CVPR19(4001-4010).
IEEE DOI
2002
Task analysis, Neural networks, Image color analysis,
Visualization, Training, Feature extraction, 3D CNN
BibRef
Wang, J.L.[Jiang-Liu],
Jiao, J.B.[Jian-Bo],
Liu, Y.H.[Yun-Hui],
Self-supervised Video Representation Learning by Pace Prediction,
ECCV20(XVII:504-521).
Springer DOI
2011
BibRef
Hejazi, S.M.[Seyed Mostafa],
Abhayaratne, C.[Charith],
Handcrafted localized phase features for human action recognition,
IVC(123), 2022, pp. 104465.
Elsevier DOI
2206
Motion analysis, Phase analysis, Human action recognition, Handcrafted features
BibRef
Geng, T.T.[Tian-Tian],
Zheng, F.[Feng],
Hou, X.R.[Xiao-Rong],
Lu, K.[Ke],
Qi, G.J.[Guo-Jun],
Shao, L.[Ling],
Spatial-Temporal Pyramid Graph Reasoning for Action Recognition,
IP(31), 2022, pp. 5484-5497.
IEEE DOI
2209
Cognition, Feature extraction, Task analysis, Kernel,
Video sequences, Image recognition, Action recognition,
spatial-temporal attention
BibRef
Tian, Y.[Yuan],
Yan, Y.C.[Yi-Chao],
Zhai, G.T.[Guang-Tao],
Guo, G.D.[Guo-Dong],
Gao, Z.Y.[Zhi-Yong],
EAN: Event Adaptive Network for Enhanced Action Recognition,
IJCV(130), No. 10, October 2022, pp. 2453-2471.
Springer DOI
2209
Code, Action Recognition.
WWW Link.
BibRef
Zhou, Y.C.[Yi-Chen],
Huang, Z.Y.[Zi-Yuan],
Yang, X.[Xulei],
Ang, M.[Marcelo],
Ng, T.K.[Teck Khim],
GCM: Efficient video recognition with glance and combine module,
PR(133), 2023, pp. 108970.
Elsevier DOI
2210
Glance and combine module, Video action recognition,
Spatio-temporal convolution, Action recognition datasets
BibRef
Hao, Y.B.[Yan-Bin],
Wang, S.[Shuo],
Tan, Y.[Yi],
He, X.N.[Xiang-Nan],
Liu, Z.G.[Zhen-Guang],
Wang, M.[Meng],
Spatio-Temporal Collaborative Module for Efficient Action Recognition,
IP(31), 2022, pp. 7279-7291.
IEEE DOI
2212
Computational modeling, Feature extraction, Convolution,
Solid modeling, Complexity theory, Collaboration,
feature contextualization
BibRef
Indhumathi, C.,
Murugan, V.,
Muthulakshmii, G.,
Human Action Recognition Using Spatio-Temporal Multiplier Network and
Attentive Correlated Temporal Feature,
IJIG(22), No. 5 2022, pp. 2250051.
DOI Link
2212
BibRef
Li, X.[Xing],
Huang, Q.[Qian],
Wang, Z.J.[Zhi-Jian],
Spatial and temporal information fusion for human action recognition
via Center Boundary Balancing Multimodal Classifier,
JVCIR(90), 2023, pp. 103716.
Elsevier DOI
2301
Human action recognition,
Gaussian pyramid depth motion images, Depth temporal maps,
Center Boundary Balancing Multimodal Classifier
BibRef
Wang, M.M.[Meng-Meng],
Xing, J.Z.[Jia-Zheng],
Su, J.[Jing],
Chen, J.[Jun],
Liu, Y.[Yong],
Learning SpatioTemporal and Motion Features in a Unified 2D Network
for Action Recognition,
PAMI(45), No. 3, March 2023, pp. 3347-3362.
IEEE DOI
2302
Spatiotemporal phenomena, Feature extraction, Optical flow, Videos,
Training, Convolution, Action recognition, frequency illustration,
twins training framework
BibRef
Xing, J.Z.[Jia-Zheng],
Wang, M.M.[Meng-Meng],
Ruan, Y.[Yudi],
Chen, B.[Bofan],
Guo, Y.[Yaowei],
Mu, B.[Boyu],
Dai, G.[Guang],
Wang, J.D.[Jing-Dong],
Liu, Y.[Yong],
Boosting Few-shot Action Recognition with Graph-guided Hybrid
Matching,
ICCV23(1740-1750)
IEEE DOI Code:
WWW Link.
2401
BibRef
Zhang, H.G.[Hui-Gang],
Wang, L.[Liuan],
Sun, J.[Jun],
Exploiting spatio-temporal knowledge for video action recognition,
IET-CV(17), No. 2, 2023, pp. 222-230.
DOI Link
2304
action recognition, commonsense knowledge, GCN, STKM
BibRef
Bai, Y.C.[Yu-Cai],
Zou, Q.[Qin],
Chen, X.Y.L.[Xie-Yuan-Li],
Li, L.X.[Ling-Xi],
Ding, Z.M.[Zheng-Ming],
Chen, L.[Long],
Extreme Low-Resolution Action Recognition with Confident
Spatial-Temporal Attention Transfer,
IJCV(131), No. 6, June 2023, pp. 1550-1565.
Springer DOI
2305
BibRef
Korban, M.[Matthew],
Youngs, P.[Peter],
Acton, S.T.[Scott T.],
A Multi-Modal Transformer network for action detection,
PR(142), 2023, pp. 109713.
Elsevier DOI
2307
Action detection, Transformer network, Optical flow, Motion features
BibRef
Korban, M.[Matthew],
Youngs, P.[Peter],
Acton, S.T.[Scott T.],
A Semantic and Motion-Aware Spatiotemporal Transformer Network for
Action Detection,
PAMI(46), No. 9, September 2024, pp. 6055-6069.
IEEE DOI
2408
Semantics, Spatiotemporal phenomena, Transformers, Videos, Encoding,
Feature extraction, Standards, Human action detection,
positional encoding
BibRef
Liu, S.C.[Shao-Can],
Ma, X.[Xin],
Attention-Driven Appearance-Motion Fusion Network for Action
Recognition,
MultMed(25), 2023, pp. 2573-2584.
IEEE DOI
2307
Optical flow, Videos, Neural networks, Feature extraction,
Spatiotemporal phenomena, 2D-single-convnet
BibRef
Qing, Z.W.[Zhi-Wu],
Zhang, S.W.[Shi-Wei],
Huang, Z.Y.[Zi-Yuan],
Xu, Y.[Yi],
Wang, X.[Xiang],
Gao, C.X.[Chang-Xin],
Jin, R.[Rong],
Sang, N.[Nong],
Self-Supervised Learning from Untrimmed Videos via Hierarchical
Consistency,
PAMI(45), No. 10, October 2023, pp. 12408-12426.
IEEE DOI
2310
BibRef
Qing, Z.W.[Zhi-Wu],
Zhang, S.W.[Shi-Wei],
Huang, Z.Y.[Zi-Yuan],
Wang, X.[Xiang],
Wang, Y.[Yuehuan],
Lv, Y.[Yiliang],
Gao, C.X.[Chang-Xin],
Sang, N.[Nong],
MAR: Masked Autoencoders for Efficient Action Recognition,
MultMed(26), 2024, pp. 218-233.
IEEE DOI
2401
BibRef
Qing, Z.W.[Zhi-Wu],
Zhang, S.W.[Shi-Wei],
Huang, Z.Y.[Zi-Yuan],
Xu, Y.[Yi],
Wang, X.[Xiang],
Tang, M.Q.[Ming-Qian],
Gao, C.X.[Chang-Xin],
Jin, R.[Rong],
Sang, N.[Nong],
Learning from Untrimmed Videos: Self-Supervised Video Representation
Learning with Hierarchical Consistency,
CVPR22(13811-13821)
IEEE DOI
2210
Representation learning, Visualization, Supervised learning,
Performance gain, Classification algorithms,
Self- semi- meta- unsupervised learning
BibRef
Huang, L.H.[Liang-Hua],
Liu, Y.[Yu],
Wang, B.[Bin],
Pan, P.[Pan],
Xu, Y.H.[Ying-Hui],
Jin, R.[Rong],
Self-Supervised Video Representation Learning by Context and Motion
Decoupling,
CVPR21(13881-13890)
IEEE DOI
2111
Feature extraction, Task analysis
BibRef
Wang, Q.[Qiang],
Zhang, Y.H.[Yan-Hao],
Zheng, Y.[Yun],
Pan, P.[Pan],
RCL: Recurrent Continuous Localization for Temporal Action Detection,
CVPR22(13556-13565)
IEEE DOI
2210
Location awareness, Detectors, Performance gain, Benchmark testing,
Action and event recognition,
Video analysis and understanding
BibRef
Mac, K.N.C.[Khoi-Nguyen C.],
Do, M.N.[Minh N.],
Vo, M.P.[Minh P.],
Efficient Human Vision Inspired Action Recognition Using Adaptive
Spatiotemporal Sampling,
IP(32), 2023, pp. 5245-5256.
IEEE DOI Code:
WWW Link.
2310
BibRef
Guo, F.T.[Fang-Tai],
Jin, T.L.[Tian-Lei],
Zhu, S.Q.[Shi-Qiang],
Xi, X.M.[Xiang-Ming],
Wang, W.[Wen],
Meng, Q.W.[Qi-Wei],
Song, W.[Wei],
Zhu, J.K.[Jia-Kai],
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention
Fusion Model for Human Action Recognition,
IP(32), 2023, pp. 4989-5003.
IEEE DOI Code:
WWW Link.
2310
BibRef
Xie, Z.[Zhao],
Chen, J.S.[Jian-Song],
Wu, K.W.[Ke-Wei],
Guo, D.[Dan],
Hong, R.C.[Ri-Chang],
Global Temporal Difference Network for Action Recognition,
MultMed(25), 2023, pp. 7594-7606.
IEEE DOI
2311
BibRef
Zhang, Y.[Yi],
Li, Y.C.[Yu-Chang],
Liu, M.W.[Ming-Wei],
Learning Self-Correlation in Space and Time as Motion Representation
for Action Recognition,
SPLetters(30), 2023, pp. 1747-1751.
IEEE DOI
2312
BibRef
Chen, Y.[Yang],
Wang, L.[Ling],
Hu, D.[Dekun],
Cheng, H.[Hong],
Multi-view graph convolution network for the recognition of human
action with spatial and temporal occlusion problems,
JVCIR(97), 2023, pp. 103957.
Elsevier DOI
2312
Human action recognition, Spatial-temporal occlusion,
Multi-view, Graph network
BibRef
Jin, Z.H.[Zhi-Hao],
Wang, Y.F.[Yi-Fan],
Wang, Q.[Qicong],
Shen, Y.[Yehu],
Meng, H.Y.[Hong-Ying],
SSRL: Self-Supervised Spatial-Temporal Representation Learning for 3D
Action Recognition,
CirSysVideo(34), No. 1, January 2024, pp. 274-285.
IEEE DOI
2401
BibRef
Li, A.[Ao],
Yi, Y.[Yang],
Liang, D.[Daan],
Residual attention fusion network for video action recognition,
JVCIR(98), 2024, pp. 103987.
Elsevier DOI
2402
Action recognition, Temporal modeling, Channel-wise attention,
Pixel-wise attention
BibRef
Raj, M.S.S.[M.S. Subodh],
George, S.N.[Sudhish N.],
Raja, K.[Kiran],
Leveraging spatio-temporal features using graph neural networks for
human activity recognition,
PR(150), 2024, pp. 110301.
Elsevier DOI
2403
Covariance descriptor, Graph neural network, Human activity, Subspace clustering
BibRef
Kim, M.[Myeongjun],
Spinola, F.[Federica],
Benz, P.[Philipp],
Kim, T.H.[Tae-Hoon],
A*: Atrous Spatial Temporal Action Recognition for Real Time
Applications,
WACV24(6999-7000)
IEEE DOI
2404
YOLO, Deep learning, Fuses, Surveillance, Face recognition,
Streaming media, Algorithms, Video recognition and understanding
BibRef
Qian, H.F.[Hui-Fang],
Zhang, J.L.[Jia-Lun],
Yi, J.P.[Jian-Ping],
Shi, Z.Y.[Zhen-Yu],
Zhang, Y.M.[Yi-Min],
CTM: Cross-time temporal module for fine-grained action recognition,
CVIU(244), 2024, pp. 104013.
Elsevier DOI
2405
Temporal context information, Fine-grained action recognition,
Spatio-temporal characteristics representation, Lightweight adaptive module
BibRef
Jiang, Y.Q.[Yu-Qin],
Popov, A.A.[Andrey A.],
Li, Z.[Zhenlong],
Hodgson, M.E.[Michael E.],
Huang, B.H.[Bing-Hu],
A Sensor-Based Simulation Method for Spatiotemporal Event Detection,
IJGI(13), No. 5, 2024, pp. 141.
DOI Link
2405
BibRef
Wang, F.[Fan],
Li, X.K.[Xin-Ke],
Xiong, H.[Han],
Mo, H.[Haofan],
Li, Y.M.[Yong-Ming],
MLENet: Multi-Level Extraction Network for video action recognition,
PR(154), 2024, pp. 110614.
Elsevier DOI
2406
Action recognition, Spatio-temporal,
Temporal feature refinement extraction module, Optical flow guided feature
BibRef
Wang, X.Y.[Xiang-Yang],
Yang, K.[Kun],
Ding, Q.[Qiang],
Wang, R.[Rui],
Sun, J.H.[Jin-Hua],
TQRFormer: Tubelet query recollection transformer for action
detection,
IVC(147), 2024, pp. 105059.
Elsevier DOI Code:
WWW Link.
2406
Spatio-temporal action detection, Transformer,
Query recollection, Matching strategy, Long-term context
BibRef
Khezerlou, F.[Fatemeh],
Baradarani, A.[Aryaz],
Balafar, M.A.[Mohammad Ali],
Maev, R.G.[Roman Gr.],
Spatio-temporal attention modules in orientation-magnitude-response
guided multi-stream CNNs for human action recognition,
IET-IPR(18), No. 9, 2024, pp. 2372-2388.
DOI Link
2407
convolutional neural nets, feature extraction,
human computer interaction, image motion analysis,
video signal processing
BibRef
Diba, A.[Ali],
Sharma, V.[Vivek],
Arzani, M.M.[Mohammad. M],
Van Gool, L.J.[Luc J.],
Spatio-Temporal Convolution-Attention Video Network,
NIVT23(859-869)
IEEE DOI
2401
BibRef
Strafforello, O.[Ombretta],
Liu, X.[Xin],
Schutte, K.[Klamer],
van Gemert, J.C.[Jan C.],
Video BagNet: short temporal receptive fields increase robustness in
long-term action recognition,
VIPriors23(159-166)
IEEE DOI
2401
BibRef
Sardari, F.[Faegheh],
Mustafa, A.[Armin],
Jackson, P.J.B.[Philip J. B.],
Hilton, A.[Adrian],
PAT: Position-Aware Transformer for Dense Multi-Label Action
Detection,
CVEU23(2980-2989)
IEEE DOI
2401
BibRef
Wang, K.C.[Kuan-Chieh],
Weng, Z.Z.[Zhen-Zhen],
Xenochristou, M.[Maria],
Araújo, J.P.[Joăo Pedro],
Gu, J.[Jeffrey],
Liu, C.K.[C. Karen],
Yeung, S.[Serena],
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the
Same Action,
CVPR23(22129-22138)
IEEE DOI
2309
BibRef
Nag, S.[Sauradip],
Zhu, X.T.[Xia-Tian],
Song, Y.Z.[Yi-Zhe],
Xiang, T.[Tao],
Post-Processing Temporal Action Detection,
CVPR23(18837-18845)
IEEE DOI
2309
BibRef
Lee, P.[Pilhyeon],
Kim, T.[Taeoh],
Shim, M.H.[Min-Ho],
Wee, D.Y.[Dong-Yoon],
Byun, H.R.[Hye-Ran],
Decomposed Cross-Modal Distillation for RGB-based Temporal Action
Detection,
CVPR23(2373-2383)
IEEE DOI
2309
BibRef
Wasim, S.T.[Syed Talal],
Khattak, M.U.[Muhammad Uzair],
Naseer, M.[Muzammal],
Khan, S.[Salman],
Shah, M.[Mubarak],
Khan, F.S.[Fahad Shahbaz],
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action
Recognition,
ICCV23(13732-13743)
IEEE DOI Code:
WWW Link.
2401
BibRef
Dave, I.R.[Ishan Rajendrakumar],
Rizve, M.N.[Mamshad Nayeem],
Chen, C.[Chen],
Shah, M.[Mubarak],
TimeBalance: Temporally-Invariant and Temporally-Distinctive Video
Representations for Semi-Supervised Action Recognition,
CVPR23(2341-2352)
IEEE DOI
2309
BibRef
Zhao, C.[Chen],
Ramazanova, M.[Merey],
Xu, M.M.[Meng-Meng],
Ghanem, B.[Bernard],
Segtad: Precise Temporal Action Detection via Semantic Segmentation,
CVEU22(576-593).
Springer DOI
2304
BibRef
Singh, G.[Gurkirt],
Choutas, V.[Vasileios],
Saha, S.[Suman],
Yu, F.[Fisher],
Van Gool, L.J.[Luc J.],
Spatio-Temporal Action Detection Under Large Motion,
WACV23(5998-6007)
IEEE DOI
2302
Tracking, Shape, Detectors, Feature extraction, Cameras
BibRef
Sui, L.[Lin],
Zhang, C.L.[Chen-Lin],
Gu, L.X.[Li-Xin],
Han, F.[Feng],
A Simple and Efficient Pipeline to Build an End-to-End
Spatial-Temporal Action Detector,
WACV23(5988-5997)
IEEE DOI
2302
Training, Codes, Computational modeling, Pipelines, Detectors,
Algorithms: Video recognition and understanding
BibRef
Kim, S.[Sangwon],
Ahn, D.[Dasom],
Ko, B.C.[Byoung Chul],
Cross-Modal Learning with 3D Deformable Attention for Action
Recognition,
ICCV23(10231-10241)
IEEE DOI
2401
BibRef
Ahn, D.[Dasom],
Kim, S.[Sangwon],
Hong, H.[Hyunsu],
Ko, B.C.[Byoung Chul],
STAR-Transformer: A Spatio-temporal Cross Attention Transformer for
Human Action Recognition,
WACV23(3319-3328)
IEEE DOI
2302
Representation learning, Computational modeling, Pose estimation,
Transformers, Skeleton, Data models, Trajectory,
visual reasoning
BibRef
Qiu, Y.[Yue],
Nagasaki, Y.[Yoshiki],
Hara, K.[Kensho],
Kataoka, H.[Hirokatsu],
Suzuki, R.[Ryota],
Iwata, K.[Kenji],
Satoh, Y.[Yutaka],
VirtualHome Action Genome: A Simulated Spatio-Temporal Scene Graph
Dataset with Consistent Relationship Labels,
WACV23(3340-3349)
IEEE DOI
2302
Location awareness, Limiting, Correlation, Costs, Annotations,
Genomics, Transformers, Robotics
BibRef
Jin, R.R.[Rong-Rong],
Ye, W.R.[Wei-Rong],
Wang, X.[Xiao],
Yan, Y.[Yan],
Wang, H.Z.[Han-Zi],
MDNet: Motion Distinction Network for Effective Action Recognition,
ICIP22(3236-3240)
IEEE DOI
2211
Image recognition, Benchmark testing, Spatiotemporal phenomena,
Action recognition, motion enhancement, video understanding
BibRef
Nag, S.[Sauradip],
Zhu, X.T.[Xia-Tian],
Deng, J.K.[Jian-Kang],
Song, Y.Z.[Yi-Zhe],
Xiang, T.[Tao],
DiffTAD: Temporal Action Detection with Proposal Denoising Diffusion,
ICCV23(10328-10340)
IEEE DOI Code:
WWW Link.
2401
BibRef
Earlier: A1, A2, A4, A5, Only:
Semi-supervised Temporal Action Detection with Proposal-Free Masking,
ECCV22(III:663-680).
Springer DOI
2211
BibRef
And: A1, A2, A4, A5, Only:
Proposal-Free Temporal Action Detection via Global Segmentation Mask
Learning,
ECCV22(III:645-662).
Springer DOI
2211
BibRef
Xiang, W.M.[Wang-Meng],
Li, C.[Chao],
Wang, B.[Biao],
Wei, X.[Xihan],
Hua, X.S.[Xian-Sheng],
Zhang, L.[Lei],
Spatiotemporal Self-Attention Modeling with Temporal Patch Shift for
Action Recognition,
ECCV22(III:627-644).
Springer DOI
2211
BibRef
Li, X.H.[Xian-Hang],
Wang, H.Y.[Hui-Yu],
Wei, C.[Chen],
Mei, J.[Jieru],
Yuille, A.L.[Alan L.],
Zhou, Y.[Yuyin],
Xie, C.[Cihang],
In Defense of Image Pre-Training for Spatiotemporal Recognition,
ECCV22(XXV:675-691).
Springer DOI
2211
BibRef
Tai, T.M.[Tsung-Ming],
Fiameni, G.[Giuseppe],
Lee, C.K.[Cheng-Kuang],
Lanz, O.[Oswald],
Higher-Order Recurrent Network with Space-Time Attention for Video
Early Action Recognition,
ICIP22(1631-1635)
IEEE DOI
2211
Visualization, Image recognition, Predictive models, Cognition,
History, Video prediction, early action recognition, space-time attention
BibRef
Foo, L.G.[Lin Geng],
Li, T.J.[Tian-Jiao],
Rahmani, H.[Hossein],
Liu, J.[Jun],
Action Detection via an Image Diffusion Process,
CVPR24(18351-18361)
IEEE DOI
2410
Casting, Image synthesis, Diffusion processes, Transformers,
Videos, Action Detection, Diffusion Model
BibRef
Li, T.J.[Tian-Jiao],
Foo, L.G.[Lin Geng],
Ke, Q.H.[Qiu-Hong],
Rahmani, H.[Hossein],
Wang, A.[Anran],
Wang, J.H.[Jing-Hua],
Liu, J.[Jun],
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action
Recognition,
ECCV22(IV:386-403).
Springer DOI
2211
BibRef
Wang, J.[Jue],
Torresani, L.[Lorenzo],
Deformable Video Transformer,
CVPR22(14033-14042)
IEEE DOI
2210
For action classification.
Deformable models, Costs, Computational modeling, Dynamics,
Predictive models, Transformers, Representation learning
BibRef
Fuad, K.A.A.[Kazi Ahmed Asif],
Martin, P.E.[Pierre-Etienne],
Giot, R.[Romain],
Bourqui, R.[Romain],
Benois-Pineau, J.[Jenny],
Zemmari, A.[Akka],
Features Understanding in 3D CNNs for Actions Recognition in Video,
IPTA20(1-6)
IEEE DOI
2206
Measurement, Correlation coefficient, Visualization, Task analysis,
Optical flow, Sports, Explainable Deep Learning, 3D convolutions,
Table Tennis
BibRef
Li, L.[Li],
Zhuang, L.S.[Lian-Sheng],
MEViT: Motion Enhanced Video Transformer for Video Classification,
MMMod22(II:419-430).
Springer DOI
2203
Use transformers rather than CNN.
BibRef
Wang, J.H.[Jia-Hao],
Chen, G.[Guo],
Huang, Y.F.[Yi-Fei],
Wang, L.M.[Li-Min],
Lu, T.[Tong],
Memory-and-Anticipation Transformer for Online Action Understanding,
ICCV23(13778-13789)
IEEE DOI Code:
WWW Link.
2401
BibRef
Liu, Z.Y.[Zhao-Yang],
Wang, L.M.[Li-Min],
Wu, W.[Wayne],
Qian, C.[Chen],
Lu, T.[Tong],
TAM: Temporal Adaptive Module for Video Recognition,
ICCV21(13688-13698)
IEEE DOI
2203
WWW Link. Adaptation models, Codes, Computational modeling, Dynamics,
Video analysis and understanding
BibRef
Kataoka, H.[Hirokatsu],
Hara, K.[Kensho],
Hayashi, R.[Ryusuke],
Yamagata, E.[Eisuke],
Inoue, N.[Nakamasa],
Spatiotemporal Initialization for 3D CNNs with Generated Motion
Patterns,
WACV22(737-746)
IEEE DOI
2202
Solid modeling, Codes,
Computational modeling, Supervised learning,
Evaluation and Comparison of Vision Algorithms
BibRef
Das, P.[Pratyusha],
Ortega, A.[Antonio],
Chen, S.[Siheng],
Mansour, H.[Hassan],
Vetro, A.[Anthony],
Application-Agnostic Spatio-Temporal Hand Graph Representations for
Stable Activity Understanding,
ICIP21(1074-1078)
IEEE DOI
2201
Measurement, Image segmentation, Motion segmentation,
Signal processing algorithms, Feature extraction, Stability analysis
BibRef
Liang, M.[Morgan],
Li, X.[Xun],
Onie, S.[Sandersan],
Larsen, M.[Mark],
Sowmya, A.[Arcot],
Improved Spatio-Temporal Action Localization for Surveillance Videos,
DICTA21(01-08)
IEEE DOI
2201
Location awareness, Surveillance, Digital images, Pipelines,
Benchmark testing, Spatiotemporal phenomena, Proposals
BibRef
Zhu, J.L.[Jin-Lei],
Chen, H.J.[Hou-Jin],
Pan, P.[Pan],
Sun, J.[Jia],
Jing, K.[Kun],
Zhang, C.F.[Chuan-Feng],
Multi-loss Spatial-Temporal Attention-Convolution Network for Action
Tube Detection,
ICIVC21(301-305)
IEEE DOI
2112
Visualization, Video sequences, Network architecture,
Feature extraction, Real-time systems, Electron tubes,
human action tube detection
BibRef
Liu, X.[Xin],
Pintea, S.L.[Silvia L.],
Nejadasl, F.K.[Fatemeh Karimi],
Booij, O.[Olaf],
van Gemert, J.C.[Jan C.],
No frame left behind: Full Video Action Recognition,
CVPR21(14887-14896)
IEEE DOI
2111
Training, Philosophical considerations, Semantics,
Memory management, Sampling methods, Nonhomogeneous media
BibRef
Sarfraz, M.S.[M. Saquib],
Murray, N.[Naila],
Sharma, V.[Vivek],
Diba, A.[Ali],
Van Gool, L.J.[Luc J.],
Stiefelhagen, R.[Rainer],
Temporally-Weighted Hierarchical Clustering for Unsupervised Action
Segmentation,
CVPR21(11220-11229)
IEEE DOI
2111
Training, Measurement, Visualization, Codes,
Spatiotemporal phenomena
BibRef
Song, X.L.[Xiao-Lin],
Zhao, S.C.[Si-Cheng],
Yang, J.Y.[Jing-Yu],
Yue, H.J.[Huan-Jing],
Xu, P.F.[Peng-Fei],
Hu, R.B.[Run-Bo],
Chai, H.[Hua],
Spatio-temporal Contrastive Domain Adaptation for Action Recognition,
CVPR21(9782-9790)
IEEE DOI
2111
Measurement, Bridges, Adaptation models,
Benchmark testing, Data models
BibRef
Bai, S.[Sikai],
Wang, Q.[Qi],
Li, X.L.[Xue-Long],
MFI: Multi-range Feature Interchange for Video Action Recognition,
ICPR21(6664-6671)
IEEE DOI
2105
Convolution, Feature extraction,
Encoding, Optical flow, Videos
BibRef
Zhou, C.H.[Cheng-Hui],
Chen, X.L.[Xiao-Lei],
Sun, P.[Pei],
Zhang, G.W.[Guan-Wen],
Zhou, W.[Wei],
Compressed Video Action Recognition Using Motion Vector Representation,
CADL20(701-713).
Springer DOI
2103
BibRef
Li, C.,
Zhang, J.,
Shan, S.,
Chen, X.,
PAS-Net: Pose-based and Appearance-based Spatiotemporal Networks
Fusion for Action Recognition,
FG20(215-221)
IEEE DOI
2102
Feature extraction, Spatiotemporal phenomena, Training
BibRef
Pan, Y.,
Sun, X.,
Wu, F.,
Enriching Optical Flow with Appearance Information for Action
Recognition,
VCIP20(251-254)
IEEE DOI
2102
Training, Adaptive optics, Feature extraction, Streaming media,
Optical flow, Optical fiber networks,
Action Recognition
BibRef
Kim, M.,
Kim, T.,
Kim, D.,
Spatio-Temporal Slowfast Self-Attention Network For Action
Recognition,
ICIP20(2206-2210)
IEEE DOI
2011
Feature extraction,
Image recognition, Convolutional neural networks, Semantics,
Atomic Visual Actions
BibRef
Kasai, S.,
Ishikawa, Y.,
Hayashi, M.,
Aoki, Y.,
Hara, K.,
Kataoka, H.,
Retrieving and Highlighting Action with Spatiotemporal Reference,
ICIP20(1401-1405)
IEEE DOI
2011
Videos, Visualization, Feature extraction,
Spatiotemporal phenomena, Task analysis, Training, Convolution,
interpretability
BibRef
Qiu, Z.,
Zhao, X.,
Hu, Z.,
Efficient Temporal-Spatial Feature Grouping For Video Action
Recognition,
ICIP20(2176-2180)
IEEE DOI
2011
Convolution, Kernel, Feature extraction, kernel decomposition
BibRef
Kepple, D.R.[Daniel R.],
Lee, D.W.[Dae-Won],
Prepsius, C.[Colin],
Isler, V.[Volkan],
Park, I.M.[Il Memming],
Lee, D.D.[Daniel D.],
Jointly Learning Visual Motion and Confidence from Local Patches in
Event Cameras,
ECCV20(VI:500-516).
Springer DOI
2011
BibRef
Wu, W.,
He, D.,
Tan, X.,
Chen, S.,
Yang, Y.,
Wen, S.,
Dynamic Inference: A New Approach Toward Efficient Video Action
Recognition,
EDLCV20(2890-2898)
IEEE DOI
2008
Computational modeling, Convolution,
Writing, Solid modeling, Feature extraction
BibRef
Yao, Y.[Yuan],
Liu, C.[Chang],
Luo, D.Z.[De-Zhao],
Zhou, Y.[Yu],
Ye, Q.X.[Qi-Xiang],
Video Playback Rate Perception for Self-Supervised Spatio-Temporal
Representation Learning,
CVPR20(6547-6556)
IEEE DOI
2008
Task analysis, Decoding, Convolution,
Image reconstruction, Semantics, Signal resolution
BibRef
Ji, J.,
Krishna, R.,
Fei-Fei, L.,
Niebles, J.C.,
Action Genome: Actions As Compositions of Spatio-Temporal Scene
Graphs,
CVPR20(10233-10244)
IEEE DOI
2008
Videos, Genomics, Bioinformatics, Task analysis, Visualization,
Cognitive science, Databases
BibRef
Kim, J.,
Cha, S.,
Wee, D.,
Bae, S.,
Kim, J.,
Regularization on Spatio-Temporally Smoothed Feature for Action
Recognition,
CVPR20(12100-12109)
IEEE DOI
2008
Training, Computational modeling,
Perturbation methods, Image recognition, Frequency modulation
BibRef
Li, X.H.[Xian-Hang],
Wang, Y.[Yali],
Zhou, Z.P.[Zhi-Peng],
Qiao, Y.[Yu],
SmallBigNet: Integrating Core and Contextual Views for Video
Classification,
CVPR20(1089-1098)
IEEE DOI
2008
Convolution, Semantics
BibRef
Wang, H.[Heng],
Tran, D.[Du],
Torresani, L.[Lorenzo],
Feiszli, M.[Matt],
Video Modeling With Correlation Networks,
CVPR20(349-358)
IEEE DOI
2008
Correlation, Optical imaging,
Optical filters, Solid modeling, Feature extraction, Optical fiber networks
BibRef
Zhao, J.J.[Jiao-Jiao],
Zhang, Y.[Yanyi],
Li, X.Y.[Xin-Yu],
Chen, H.[Hao],
Shuai, B.[Bing],
Xu, M.Z.[Ming-Ze],
Liu, C.H.[Chun-Hui],
Kundu, K.[Kaustav],
Xiong, Y.J.[Yuan-Jun],
Modolo, D.[Davide],
Marsic, I.[Ivan],
Snoek, C.G.M.[Cees G.M.],
Tighe, J.[Joseph],
TubeR: Tubelet Transformer for Video Action Detection,
CVPR22(13588-13597)
IEEE DOI
2210
Location awareness, Context-aware services, Codes, Switches,
Detectors, Transformers, Action and event recognition,
Video analysis and understanding
BibRef
Li, X.Y.[Xin-Yu],
Liu, C.H.[Chun-Hui],
Shuai, B.[Bing],
Zhu, Y.[Yi],
Chen, H.[Hao],
Tighe, J.[Joseph],
NUTA: Non-uniform Temporal Aggregation for Action Recognition,
WACV22(827-836)
IEEE DOI
2202
Visualization, Solid modeling, Aggregates,
Feature extraction, Synchronization, Task analysis,
Action and Behavior Recognition Motion Processing
BibRef
Li, X.Y.[Xin-Yu],
Shuai, B.[Bing],
Tighe, J.[Joseph],
Directional Temporal Modeling for Action Recognition,
ECCV20(VI:275-291).
Springer DOI
2011
BibRef
Wang, Z.[Zhe],
Chen, H.[Hao],
Li, X.Y.[Xin-Yu],
Liu, C.H.[Chun-Hui],
Xiong, Y.J.[Yuan-Jun],
Tighe, J.[Joseph],
Fowlkes, C.[Charless],
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised
Temporal Action Segmentation,
WACV22(175-184)
IEEE DOI
2202
Training, Representation learning, Correlation,
Clustering algorithms, Benchmark testing, Feature extraction,
Action and Behavior Recognition
BibRef
Martínez, B.M.,
Modolo, D.,
Xiong, Y.J.[Yuan-Jun],
Tighe, J.[Joseph],
Action Recognition With Spatial-Temporal Discriminative Filter Banks,
ICCV19(5481-5490)
IEEE DOI
2004
channel bank filters, image recognition, image representation,
object recognition, Aggregates
BibRef
Tavakolian, M.,
Tavakoli, H.R.,
Hadid, A.,
AWSD: Adaptive Weighted Spatiotemporal Distillation for Video
Representation,
ICCV19(8019-8028)
IEEE DOI
2004
Code, Video Analysis.
WWW Link. Gaussian processes, image classification, image representation,
image segmentation, spatiotemporal phenomena,
Covariance matrices
BibRef
Zhao, H.,
Wildes, R.P.[Richard P.],
Spatiotemporal Feature Residual Propagation for Action Prediction,
ICCV19(7002-7011)
IEEE DOI
2004
image filtering, image motion analysis, image recognition,
image representation, Kalman filters, spatiotemporal phenomena, Training
BibRef
Seong, H.J.[Hong-Je],
Hyun, J.[Junhyuk],
Kim, E.T.[Eun-Tai],
Video Multitask Transformer Network,
CoView19(1553-1561)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, image fusion, image motion analysis, untrimmed video
BibRef
Girdhar, R.,
Tran, D.,
Torresani, L.,
Ramanan, D.,
DistInit: Learning Video Representations Without a Single Labeled
Video,
ICCV19(852-861)
IEEE DOI
2004
image classification, image representation,
learning (artificial intelligence), spatiotemporal phenomena,
Computational modeling
BibRef
Jiang, B.,
Wang, M.,
Gan, W.,
Wu, W.,
Yan, J.,
STM: SpatioTemporal and Motion Encoding for Action Recognition,
ICCV19(2000-2009)
IEEE DOI
2004
feature extraction, image motion analysis, image recognition,
learning (artificial intelligence), neural nets,
Computer architecture
BibRef
Meng, L.,
Zhao, B.,
Chang, B.,
Huang, G.,
Sun, W.,
Tung, F.,
Sigal, L.,
Interpretable Spatio-Temporal Attention for Video Action Recognition,
HVU19(1513-1522)
IEEE DOI
2004
feature extraction, image classification, image motion analysis,
image representation, image sequences,
Spatio temporal attention
BibRef
Materzynska, J.,
Xiao, T.,
Herzig, R.,
Xu, H.,
Wang, X.,
Darrell, T.J.,
Something-Else: Compositional Action Recognition With
Spatial-Temporal Interaction Networks,
CVPR20(1046-1056)
IEEE DOI
2008
Videos, Cognition, Training, Task analysis, Feature extraction,
Detectors, Computational modeling
BibRef
Herzig, R.,
Levi, E.,
Xu, H.,
Gao, H.,
Brosh, E.,
Wang, X.,
Globerson, A.,
Darrell, T.J.,
Spatio-Temporal Action Graph Networks,
ADW19(2347-2356)
IEEE DOI
2004
graph theory, image representation,
learning (artificial intelligence), video signal processing, Collisions
BibRef
Piergiovanni, A.J.,
Angelova, A.,
Toshev, A.,
Ryoo, M.S.,
Evolving Space-Time Neural Architectures for Videos,
ICCV19(1793-1802)
IEEE DOI
2004
convolutional neural nets, evolutionary computation,
image representation, neural net architecture,
Kinetic theory
BibRef
Piergiovanni, A.J.,
Ryoo, M.S.[Michael S.],
Recognizing Actions in Videos from Unseen Viewpoints,
CVPR21(4122-4130)
IEEE DOI
2111
BibRef
Earlier:
Learning Multimodal Representations for Unseen Activities,
WACV20(506-515)
IEEE DOI
2006
BibRef
Earlier:
Representation Flow for Action Recognition,
CVPR19(9937-9945).
IEEE DOI
2002
Training, Training data, Cameras, Data models.
Videos, Decoding, Task analysis, Activity recognition, Generators
BibRef
Yang, X.T.[Xi-Tong],
Yang, X.D.[Xiao-Dong],
Liu, M.Y.[Ming-Yu],
Xiao, F.Y.[Fan-Yi],
Davis, L.S.[Larry S.],
Kautz, J.[Jan],
STEP: Spatio-Temporal Progressive Learning for Video Action Detection,
CVPR19(264-272).
IEEE DOI
2002
BibRef
Song, L.[Lin],
Zhang, S.W.[Shi-Wei],
Yu, G.[Gang],
Sun, H.B.[Hong-Bin],
TACNet: Transition-Aware Context Network for Spatio-Temporal Action
Detection,
CVPR19(11979-11987).
IEEE DOI
2002
BibRef
Li, C.[Chao],
Zhong, Q.Y.[Qiao-Yong],
Xie, D.[Di],
Pu, S.L.[Shi-Liang],
Collaborative Spatiotemporal Feature Learning for Video Action
Recognition,
CVPR19(7864-7873).
IEEE DOI
2002
BibRef
Park, J.,
Lee, J.,
Jeon, S.,
Kim, S.,
Sohn, K.,
Graph Regularization Network with Semantic Affinity for
Weakly-Supervised Temporal Action Localization,
ICIP19(3701-3705)
IEEE DOI
1910
weakly-supervised temporal action localization,
graph Laplacian regularization, semantic affinity
BibRef
Kong, J.,
Xu, R.,
Xing, J.,
Li, K.,
Ma, W.,
Spatial Temporal Attentional Glimpse for Human Activity
Classification in Video,
ICIP19(4040-4044)
IEEE DOI
1910
Human Action, Classification, Deep Learning
BibRef
Gleason, J.[Joshua],
Ranjan, R.[Rajeev],
Schwarcz, S.[Steven],
Castillo, C.[Carlos],
Chen, J.C.[Jun-Cheng],
Chellappa, R.[Rama],
A Proposal-Based Solution to Spatio-Temporal Action Detection in
Untrimmed Videos,
WACV19(141-150)
IEEE DOI
1904
feature extraction, image classification,
image colour analysis, image motion analysis,
Automobiles
BibRef
Ahsan, U.,
Madhok, R.,
Essa, I.,
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for
Video Action Recognition,
WACV19(179-189)
IEEE DOI
1904
image recognition, image sequences, unsupervised learning,
video signal processing, spatiotemporal context,
Spatiotemporal phenomena
BibRef
Aakur, S.N.[Sathyanarayanan N.],
Sawyer, D.[Daniel],
Sarkar, S.[Sudeep],
Fine-grained Action Detection in Untrimmed Surveillance Videos,
HADCV19(38-40)
IEEE DOI
1902
Videos, Proposals, Feature extraction,
Spatiotemporal phenomena, Object detection
BibRef
Hara, K.,
Kataoka, H.,
Satoh, Y.,
Towards Good Practice for Action Recognition with Spatiotemporal 3D
Convolutions,
ICPR18(2516-2521)
IEEE DOI
1812
Training, Videos, Kinetic theory, Kernel
BibRef
Tran, D.,
Wang, H.,
Torresani, L.,
Ray, J.,
Le Cun, Y.,
Paluri, M.,
A Closer Look at Spatiotemporal Convolutions for Action Recognition,
CVPR18(6450-6459)
IEEE DOI
1812
Spatiotemporal phenomena, Solid modeling, Feature extraction,
Computer architecture
BibRef
Diba, A.[Ali],
Fayyaz, M.[Mohsen],
Sharma, V.[Vivek],
Arzani, M.M.[M. Mahdi],
Yousefzadeh, R.[Rahman],
Gall, J.[Juergen],
Van Gool, L.J.[Luc J.],
Spatio-temporal Channel Correlation Networks for Action Classification,
ECCV18(II: 299-315).
Springer DOI
1810
BibRef
Duan, X.,
Wang, L.,
Zhai, C.,
Zheng, N.,
Zhang, Q.,
Niu, Z.,
Hua, G.,
Joint Spatio-Temporal Action Localization in Untrimmed Videos with
Per-Frame Segmentation,
ICIP18(918-922)
IEEE DOI
1809
Videos, Detectors, Proposals,
Image color analysis, Optimization, Testing, Action Localization, LSTM
BibRef
Yang, H.,
He, X.,
Porikli, F.M.,
Instance-Aware Detailed Action Labeling in Videos,
WACV18(1577-1586)
IEEE DOI
1806
feature extraction, image colour analysis, image fusion,
learning (artificial intelligence), object detection,
Videos
BibRef
Zhou, K.,
Zhu, Y.,
Zhao, Y.,
A spatio-temporal deep architecture for surveillance event detection
based on ConvLSTM,
VCIP17(1-4)
IEEE DOI
1804
feature extraction,
learning (artificial intelligence), object detection,
Surveillance Video
BibRef
Wu, Q.,
Quo, H.,
Wu, X.,
Zhou, Y.,
Li, N.,
Fast action localization based on spatio-temporal path search,
ICIP17(3350-3354)
IEEE DOI
1803
Dynamic programming, Estimation, Measurement, Proposals,
Real-time systems, Task analysis, Videos, Action localization,
Spatiotemporal path
BibRef
Yadav, G.K.,
Sethi, A.,
Action recognition using spatio-temporal differential motion,
ICIP17(3415-3419)
IEEE DOI
1803
Cameras, Databases, Feature extraction, Integrated optics,
Streaming media, Training, Video sequences,
optical flow
BibRef
Zhu, H.Y.[Hong-Yuan],
Vial, R.[Romain],
Lu, S.J.[Shi-Jian],
TORNADO: A Spatio-Temporal Convolutional Regression Network for Video
Action Proposal,
ICCV17(5814-5822)
IEEE DOI
1802
convolution, image motion analysis, object detection,
recurrent neural nets, regression analysis,
BibRef
Singh, G.,
Saha, S.,
Sapienza, M.[Michael],
Torr, P.H.S.[Philip H.S.],
Cuzzolin, F.[Fabio],
Online Real-Time Multiple Spatiotemporal Action Localisation and
Prediction,
ICCV17(3657-3666)
IEEE DOI
1802
feature extraction, image classification,
learning (artificial intelligence), object detection,
Streaming media
BibRef
Saha, S.,
Singh, G.,
Cuzzolin, F.,
AMTnet:
Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture,
ICCV17(4424-4433)
IEEE DOI
1802
convolution, feature extraction, image classification,
image motion analysis, image recognition, image representation,
Training
BibRef
Hara, K.,
Kataoka, H.,
Satoh, Y.,
Learning Spatio-Temporal Features with 3D Residual Networks for
Action Recognition,
EmotionComp17(3154-3160)
IEEE DOI
1802
Databases, Kernel, Kinetic theory,
Training, Videos
BibRef
Stroud, J.C.,
Ross, D.A.,
Sun, C.,
Deng, J.,
Sukthankar, R.,
D3D: Distilled 3D Networks for Video Action Recognition,
WACV20(614-623)
IEEE DOI
2006
Integrated optics, Task analysis,
Solid modeling, Optical fiber networks, Training, Kinetic theory
BibRef
Jiang, Z.L.[Zhuo-Lin],
Rozgic, V.[Viktor],
Adali, S.[Sancar],
Learning Spatiotemporal Features for Infrared Action Recognition with
3D Convolutional Neural Networks,
PBVS17(309-317)
IEEE DOI
1709
Convolutional codes, Image recognition,
Optical imaging, Solid modeling, Videos
BibRef
Tu, Z.,
Cao, J.[Jun],
Li, Y.[Yikang],
Li, B.,
MSR-CNN:
Applying motion salient region based descriptors for action recognition,
ICPR16(3524-3529)
IEEE DOI
1705
Feature extraction, Optical imaging,
Sparse matrices, Tracking, Trajectory,
Action recognition, Convolutional Neural Networks, Motion, salient, regions
BibRef
Aydin, B.,
Angryk, R.A.,
Spatiotemporal event sequence mining from evolving regions,
ICPR16(4172-4177)
IEEE DOI
1705
Algorithm design and analysis, Extraterrestrial measurements,
Geometry, Indexes, Spatiotemporal phenomena, TV, Trajectory,
Event Sequence Mining, Sequence Patterns, Spatiotemporal,
Knowledge, Discovery
BibRef
Li, N.N.[Nan-Nan],
Xu, D.[Dan],
Ying, Z.Q.[Zhen-Qiang],
Li, Z.H.[Zhi-Hao],
Li, G.[Ge],
Searching Action Proposals via Spatial Actionness Estimation and
Temporal Path Inference and Tracking,
ACCV16(II: 384-399).
Springer DOI
1704
BibRef
Duta, I.C.[Ionut C.],
Ionescu, B.[Bogdan],
Aizawa, K.[Kiyoharu],
Sebe, N.[Nicu],
Spatio-Temporal Vector of Locally Max Pooled Features for Action
Recognition in Videos,
CVPR17(3205-3214)
IEEE DOI
1711
BibRef
And:
Spatio-Temporal VLAD Encoding for Human Action Recognition in Videos,
MMMod17(I: 365-378).
Springer DOI
1701
Encoding, Feature extraction,
Pipelines, Videos, Visualization
BibRef
Ye, Y.C.[Yuan-Cheng],
Tian, Y.L.[Ying-Li],
Embedding Sequential Information into Spatiotemporal Features for
Action Recognition,
Robust16(1110-1118)
IEEE DOI
1612
BibRef
Belhadj, L.C.,
Mignotte, M.,
Spatio-temporal fastmap-based mapping for human action recognition,
ICIP16(3046-3050)
IEEE DOI
1610
Correlation
BibRef
Ji, X.P.[Xiao-Peng],
Cheng, J.[Jun],
Tao, D.P.[Da-Peng],
Local mean spatio-temporal feature for depth image-based speed-up
action recognition,
ICIP15(2389-2393)
IEEE DOI
1512
Speed-up action recognition
BibRef
Liang, B.[Bin],
Zheng, L.H.[Li-Hong],
Spatio-temporal pyramid cuboid matching for action recognition using
depth maps,
ICIP15(2070-2074)
IEEE DOI
1512
Action recognition; Cuboid fusion; PMHT; STPCM
BibRef
Zhang, T.[Tao],
Xu, L.[Long],
Yang, J.[Jie],
Shi, P.F.[Peng-Fei],
Jia, W.J.[Wen-Jing],
Sparse coding-based spatiotemporal saliency for action recognition,
ICIP15(2045-2049)
IEEE DOI
1512
Shannon information entropy
BibRef
Trichet, R.[Remi],
O'Connor, N.E.[Noel E.],
TREAT: Terse Rapid Edge-Anchored Tracklets,
AVSS16(400-406)
IEEE DOI
1611
Computational efficiency
BibRef
Jargalsaikhan, I.[Iveel],
Little, S.[Suzanne],
O'Connor, N.E.[Noel E.],
Action localization in video using a graph-based feature
representation,
AVSS17(1-6)
IEEE DOI
1806
feature extraction, graph theory, image motion analysis,
image recognition, image representation, image sequences,
Video sequences
BibRef
Jargalsaikhan, I.[Iveel],
Little, S.[Suzanne],
Trichet, R.[Remi],
O'Connor, N.E.[Noel E.],
Action recognition in video using a spatial-temporal graph-based
feature representation,
AVSS15(1-6)
IEEE DOI
1511
Clustering algorithms
BibRef
Kardaris, N.,
Pitsikalis, V.,
Mavroudi, E.,
Maragos, P.,
Introducing temporal order of dominant visual word sub-sequences for
human action recognition,
ICIP16(3061-3065)
IEEE DOI
1610
Computational modeling
BibRef
Maninis, K.[Kevis],
Koutras, P.[Petros],
Maragos, P.[Petros],
Advances on action recognition in videos using an interest point
detector based on multiband spatio-temporal energies,
ICIP14(1490-1494)
IEEE DOI
1502
Accuracy
BibRef
Georgakis, C.[Christos],
Maragos, P.[Petros],
Evangelopoulos, G.[Georgios],
Dimitriadis, D.[Dimitrios],
Dominant spatio-temporal modulations and energy tracking in videos:
Application to interest point detection for action recognition,
ICIP12(741-744).
IEEE DOI
1302
BibRef
Han, T.T.[Ting-Ting],
Yao, H.X.[Hong-Xun],
Zhang, Y.H.[Yan-Hao],
Xu, P.F.[Peng-Fei],
A spatial-temporal constraint-based action recognition method,
ICIP13(2767-2771)
IEEE DOI
1402
Action recognition
BibRef
Sun, Q.R.[Qian-Ru],
Liu, H.[Hong],
Learning spatio-temporal co-occurrence correlograms for efficient
human action classification,
ICIP13(3220-3224)
IEEE DOI
1402
Human action classification
BibRef
Zhang, L.[Lei],
Wang, T.[Tao],
Zhen, X.T.[Xian-Tong],
Recognizing actions via sparse coding on structure projection,
ICIP13(2412-2415)
IEEE DOI
1402
Spatio-temporal steerable detector
BibRef
Zhang, X.J.[Xiao-Jing],
Zhang, H.[Hua],
Cao, X.C.[Xiao-Chun],
Action recognition based on spatial-temporal pyramid sparse coding,
ICPR12(1455-1458).
WWW Link.
1302
BibRef
Mesmakhosroshahi, M.[Maral],
Kim, J.[Joohee],
Improving spatio-temporal feature extraction techniques and their
applications in action classification,
VCIP12(1-6).
IEEE DOI
1302
BibRef
Mcardle, G.,
Tahir, A.,
Bertolotto, M.,
Spatio-temporal Clustering of Movement Data: An Application to
Trajectories Generated by Human-Computer Interaction,
AnnalsPRS(I-2), No. 2012, pp. 147-152.
DOI Link
1209
BibRef
Souza, F.[Fillipe],
Valle, E.[Eduardo],
Chávez, G.[Guillermo],
de Albuquerque Araújo, A.[Arnaldo],
Color-Aware Local Spatiotemporal Features for Action Recognition,
CIARP11(248-255).
Springer DOI
1111
BibRef
Baccouche, M.[Moez],
Mamalet, F.[Franck],
Wolf, C.[Christian],
Garcia, C.[Christophe],
Baskurt, A.[Atilla],
Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence
Classification,
BMVC12(124).
DOI Link
1301
BibRef
Yan, X.[Xunshi],
Luo, Y.P.[Yu-Pin],
Making full use of spatial-temporal interest points:
An AdaBoost approach for action recognition,
ICIP10(4677-4680).
IEEE DOI
1009
BibRef
Hu, Q.[Qiong],
Qin, L.[Lei],
Huang, Q.M.[Qing-Ming],
Jiang, S.Q.[Shu-Qiang],
Tian, Q.[Qi],
Action Recognition Using Spatial-Temporal Context,
ICPR10(1521-1524).
IEEE DOI
1008
BibRef
Utasi, Á.[Ákos],
Kovács, A.[Andrea],
Recognizing Human Actions by Using Spatio-temporal Motion Descriptors,
ACIVS10(II: 366-375).
Springer DOI
1012
BibRef
Zhong, Y.[Yu],
Stevens, M.[Mark],
Action recognition in spatiotemporal volume,
VAM10(25-30).
IEEE DOI
1006
Patterns in S-T volume.
BibRef
Sawant, N.[Nikhil],
Biswas, K.K.,
Human Action Recognition Based on Spatio-temporal Features,
PReMI09(357-362).
Springer DOI
0912
BibRef
Sun, J.[Ju],
Wu, X.[Xiao],
Yan, S.C.[Shui-Cheng],
Cheong, L.F.[Loong-Fah],
Chua, T.S.[Tat-Seng],
Li, J.T.[Jin-Tao],
Hierarchical spatio-temporal context modeling for action recognition,
CVPR09(2004-2011).
IEEE DOI
0906
BibRef
Rodriguez, M.D.[Mikel D.],
Ahmed, J.[Javed],
Shah, M.[Mubarak],
Action MACH a spatio-temporal Maximum Average Correlation Height filter
for action recognition,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Sun, L.,
Jia, K.[Kui],
Yeung, D.Y.[Dit-Yan],
Shi, B.E.,
Human Action Recognition Using Factorized Spatio-Temporal
Convolutional Networks,
ICCV15(4597-4605)
IEEE DOI
1602
Computer architecture
BibRef
Batra, D.[Dhruv],
Chen, T.H.[Tsu-Han],
Sukthankar, R.[Rahul],
Space-Time Shapelets for Action Recognition,
Motion08(1-6).
IEEE DOI
0801
BibRef
Jia, K.[Kui],
Yeung, D.Y.[Dit-Yan],
Human action recognition using Local Spatio-Temporal Discriminant
Embedding,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Patron-Perez, A.[Alonso],
Reid, I.D.[Ian D.],
Patron, A.,
Reid, I.D.,
A Probabilistic Framework for Recognizing Similar Actions using
Spatio-Temporal Features,
BMVC07(xx-yy).
PDF File.
0709
BibRef
Cuntoor, N.P.[Naresh P.],
Morse Functions for Activity Classification Using Spatiotemporal
Volumes,
BP06(20).
IEEE DOI
0609
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Action Recognition and Detection, Surveys, Evaluation, General .