16.7.4.6.4 Spatio-Temporal Techniques for Human Action Recognition and Detection

Chapter Contents (Back)
Action Recognition. Human Actions. Spatio-Temporal. See also Motion Flow, Motion Vectors for Human Action Recognition and Detection.

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

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

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

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 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

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
computer vision, 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. Computer architecture, Convolution, Feature extraction, Training, spatial-temporal decomposition BibRef

Yu, T.Z.[Ting-Zhao], Wang, L.F.[Ling-Feng], Guo, C.[Chaoxu], 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
computer vision, 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

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, Computer architecture, Task analysis, deep convolutional networks 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.[Linchao], Tu, Z.G.[Zhi-Gang], Zhang, S.[Shifu], Han, F.[Fei], Yang, B.[Boxiong],
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

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


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

Martínez, B.M., Modolo, D., Xiong, Y., Tighe, J.,
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.[Hongje], Hyun, J.[Junhyuk], Kim, E.[Euntai],
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.,
Learning Multimodal Representations for Unseen Activities,
WACV20(506-515)
IEEE DOI 2006
Videos, Decoding, Task analysis, Training, Activity recognition, Generators BibRef

Piergiovanni, A.J., Ryoo, M.S.[Michael S.],
Representation Flow for Action Recognition,
CVPR19(9937-9945).
IEEE DOI 2002
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

Wang, J.[Jiangliu], Jiao, J.B.[Jian-Bo], Bao, L.[Linchao], He, S.F.[Sheng-Feng], Liu, Y.[Yunhui], Liu, W.[Wei],
Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics,
CVPR19(4001-4010).
IEEE DOI 2002
BibRef

Song, L.[Lin], Zhang, S.[Shiwei], 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

Zhang, P., Cao, Y., Liu, B.,
Tream Single Shot Spatial-Temporal Action Detection,
ICIP19(3691-3695)
IEEE DOI 1910
Action Detection, spatial-temporal action localization, 3D convolutional neural networks, SSD BibRef

Liu, Z.Y.[Zi-Yi], Wang, L.[Le], Zhang, Q.L.[Qi-Lin], Gao, Z.N.[Zhan-Ning], Niu, Z.X.[Zhen-Xing], Zheng, N.N.[Nan-Ning], Hua, G.[Gang],
Weakly Supervised Temporal Action Localization Through Contrast Based Evaluation Networks,
ICCV19(3898-3907)
IEEE DOI 2004
image classification, video signal processing, action proposal evaluator, Streaming media BibRef

Zhai, Y.H.[Yuan-Hao], Wang, L.[Le], Liu, Z.Y.[Zi-Yi], Zhang, Q.L.[Qi-Lin], Hua, G.[Gang], Zheng, N.N.[Nan-Ning],
Action Coherence Network for Weakly Supervised Temporal Action Localization,
ICIP19(3696-3700)
IEEE DOI 1910
weakly-supervised, temporal action lo-calization, coherence loss 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
computer vision, 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, Conferences, Computer vision, 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., LeCun, 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
computer vision, 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
Computer architecture, 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, Pattern recognition, 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.[Lihong],
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.
HTML Version. 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

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 -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Human Action Recognition and Detection, Surveys, Evaluation, General .


Last update:Oct 19, 2020 at 15:02:28