16.7.4.6.2 Spatio-Temporal Techniques for Human Action Recognition and Detection

Chapter Contents (Back)
Action Recognition. Human Actions. Spatio-Temporal.

Bobick, A.F.[Aaron F.], Davis, J.W.[James W.],
The Recognition of Human Movement Using Temporal Templates,
PAMI(23), No. 3, March 2001, pp. 257-267.
IEEE DOI 0103
BibRef
Earlier:
Action Recognition Using Temporal Templates,
MBR97(Chapter 6). BibRef
Earlier:
Real-Time Recognition of Activity Using Temporal Templates,
WACV96(39-42).
IEEE DOI 9609
BibRef
And: Vismod-386, 1997.
HTML Version. BibRef
And: A2, A1:
The Representation and Recognition of Action Using Temporal Templates,
CVPR97(928-934).
IEEE DOI 9704
BibRef
And: Vismod--402, 1997.
HTML Version. BibRef
Earlier: A1, A2:
An Appearance-Based Representation of Action,
ICPR96(I: 307-312).
IEEE DOI 9608
Actions (human motion). Templates. A temporal template: a vector image where the value at each point is a funciton of the motion properties at the corresponding location in the image sequence. MIT BibRef

Davis, J.W.[James W.],
Sequential Reliable-Inference for Rapid Detection of Human Actions,
EventVideo04(111).
IEEE DOI 0502
BibRef
Earlier:
Hierarchical Motion History Images for Recognizing Human Motion,
EventVideo01(39-46).
IEEE DOI 0106
BibRef

Davis, J.W.[James W.],
Appearance-Based Motion Recognition of Human Actions,
Vismod-387, 1996, M.S. Thesis Index on the spatial distribution of motion energy.
HTML Version. BibRef 9600

Davis, J.W.[James W.], Tyagi, A.[Ambrish],
Minimal-latency human action recognition using reliable-inference,
IVC(24), No. 5, 1 May 2006, pp. 455-472.
Elsevier DOI 0606
BibRef
Earlier:
A reliable-inference framework for recognition of human actions,
AVSBS03(169-176).
IEEE DOI 0310
Action recognition; Reliable-inference; MAP; Video analysis Determine shortest video sequence, and extend if confusing or unreliable. BibRef

Davis, J.W.[James W.], Gao, H.[Hui],
An expressive three-mode principal components model of human action style,
IVC(21), No. 11, October 2003, pp. 1001-1016.
Elsevier DOI 0310
BibRef
Earlier:
Recognizing human action efforts: An adaptive three-mode PCA framework,
ICCV03(1463-1469).
IEEE DOI 0311
BibRef

Davis, J.W., Gao, H.[Hui], Kannappan, V.S.,
A three-mode expressive feature model of action effort,
Motion02(139-144).
IEEE DOI 0303
How much effort is required for the action. 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

Ahad, M.A.R.[M. Atiqur Rahman], Tan, J.K., Kim, H.S., Ishikawa, S.,
Temporal motion recognition and segmentation approach,
IJIST(19), No. 2, June 2009, pp. 91-99.
DOI Link 0905
BibRef

Ahad, M.A.R.[M. Atiqur Rahman], Tan, J.K., Kim, H.S., Ishikawa, S.,
Motion history image: Its variants and applications,
MVA(23), No. 2, March 2012, pp. 255-281.
WWW Link. 1202
BibRef
Earlier:
Action recognition by employing combined directional motion history and energy images,
CVCGI10(73-78).
IEEE DOI 1006
BibRef

Ahad, M.A.R.[M. Atiqur Rahman],
Computer Vision and Action Recognition: A Guide for Image Processing and Computer Vision Community for Action Understanding,
SpringerNew-York, 2012.

ISBN: 978-94-91216-19-0.
WWW Link. 1202
Action Datasets; Action Representation Approaches (Statistical or Structural); Shape Representation and Feature Vector Analysis (Tracking issues); and Challenges Ahead BibRef

Ahad, M.A.R.[M. Atiqur Rahman],
Motion History Images for Action Recognition and Understanding,

Springer2013. ISBN 978-1-4471-4729-9
WWW Link. 1304
Survey, Motion History Image. Motion history image (MHI) method. BibRef

Ahad, M.A.R.[M. Atiqur Rahman], Ogata, T., Tan, J.K., Kim, H.S., Ishikawa, S.,
Motion recognition approach to solve overwriting in complex actions,
FG08(1-6).
IEEE DOI 0809
BibRef

Ahad, M.A.R.[M. Atiqur Rahman],
Smart Approaches for Human Action Recognition,
PRL(34), No. 15, 2013, pp. 1769-1770.
Elsevier DOI 1309
BibRef

Mahbub, U.[Upal], Imtiaz, H.[Hafiz], Ahad, M.A.R.[M. Atiqur Rahman],
Action recognition based on statistical analysis from clustered flow vectors,
SIViP(8), No. 2, February 2014, pp. 243-253.
WWW Link. 1402
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

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

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

Ballan, L.[Lamberto], Bertini, M.[Marco], del Bimbo, A.[Alberto], Seidenari, L.[Lorenzo], Serra, G.[Giuseppe],
Effective Codebooks for Human Action Representation and Classification in Unconstrained Videos,
MultMed(14), No. 4, 2012, pp. 1234-1245.
IEEE DOI 1208
BibRef
Earlier:
Effective Codebooks for human action categorization,
ObjectEvent09(506-513).
IEEE DOI 0910
BibRef
And:
Recognizing human actions by fusing spatio-temporal appearance and motion descriptors,
ICIP09(3569-3572).
IEEE DOI 0911
BibRef

Uricchio, T.[Tiberio], Ballan, L.[Lamberto], Seidenari, L.[Lorenzo], del Bimbo, A.[Alberto],
Automatic image annotation via label transfer in the semantic space,
PR(71), No. 1, 2017, pp. 144-157.
Elsevier DOI 1707
Automatic, image, annotation BibRef

Uricchio, T.[Tiberio], Bertini, M.[Marco], Seidenari, L.[Lorenzo], del Bimbo, A.[Alberto],
Fisher Encoded Convolutional Bag-of-Windows for Efficient Image Retrieval and Social Image Tagging,
WSM15(1020-1026)
IEEE DOI 1602
Aggregate a set of Deep Convolutional Neural Network (CNN) responses, extracted from a set of image windows. BibRef

Uricchio, T.[Tiberio], Ballan, L.[Lamberto],
Evaluating Temporal Information for Social Image Annotation and Retrieval,
CIAP13(I:722-732).
Springer DOI 1311
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

Seidenari, L.[Lorenzo], Serra, G.[Giuseppe], Bagdanov, A.D., del Bimbo, A.[Alberto],
Local Pyramidal Descriptors for Image Recognition,
PAMI(36), No. 5, May 2014, pp. 1033-1040.
IEEE DOI 1405
Approximation methods BibRef

Costantini, L.[Luca], Seidenari, L.[Lorenzo], Serra, G.[Giuseppe], Capodiferro, L.[Licia], del Bimbo, A.[Alberto],
Space-Time Zernike Moments and Pyramid Kernel Descriptors for Action Classification,
CIAP11(II: 199-208).
Springer DOI 1109
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

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

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

Kulkarni, K.[Kaustubh], Evangelidis, G.[Georgios], Cech, J.[Jan], Horaud, R.[Radu],
Continuous Action Recognition Based on Sequence Alignment,
IJCV(112), No. 1, March 2015, pp. 90-114.
Springer DOI 1503
BibRef
And: Erratum: IJCV(112), No. 1, March 2015, pp. 130.
Springer DOI 1503
See also Continuous Gesture Recognition from Articulated Poses. 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

Tsai, D.M.[Du-Ming], Chiu, W.Y.[Wei-Yao], Lee, M.H.[Men-Han],
Optical flow-motion history image (OF-MHI) for action recognition,
SIViP(9), No. 8, November 2015, pp. 1897-1906.
WWW Link. 1511
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

Wang, X.F.[Xiao-Fang], Qi, C.[Chun],
Action recognition using edge trajectories and motion acceleration descriptor,
MVA(27), No. 5, August 2016, pp. 861-875.
WWW Link. 1609
BibRef

Wang, X.F.[Xiao-Fang], Qi, C.[Chun],
Saliency-based dense trajectories for action recognition using low-rank matrix decomposition,
JVCIR(41), No. 1, 2016, pp. 361-374.
Elsevier DOI 1612
Action recognition BibRef

Wang, X.F.[Xiao-Fang], Qi, C.[Chun], Lin, F.[Fei],
Combined trajectories for action recognition based on saliency detection and motion boundary,
SP:IC(57), No. 1, 2017, pp. 91-102.
Elsevier DOI 1709
Action recognition BibRef

Liu, Y.[Yinan], 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

Hara, K.[Kensho], Hirayama, T.[Takatsugu], Mase, K.[Kenji],
Vote Distribution Model for Hough-Based Action Detection,
IEICE(E99-D), No. 11, November 2016, pp. 2796-2808.
WWW Link. 1611
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

Qin, J.[Jie], Liu, L.[Li], Yu, M.Y.[Meng-Yang], Wang, Y.H.[Yun-Hong], Shao, L.[Ling],
Fast Action Retrieval from Videos via Feature Disaggregation,
CVIU(156), No. 1, 2017, pp. 104-116.
Elsevier DOI 1702
BibRef
Earlier: BMVC15(xx-yy).
DOI Link 1601
Similarity search BibRef

Fernando, B.[Basura], Gavves, E.[Efstratios], Oramas Mogrovejo, J.A.[José Antonio], Ghodrati, A.[Amir], Tuytelaars, T.[Tinne],
Rank Pooling for Action Recognition,
PAMI(39), No. 4, April 2017, pp. 773-787.
IEEE DOI 1703
BibRef
Earlier:
Modeling video evolution for action recognition,
CVPR15(5378-5387)
IEEE DOI 1510
Data models BibRef

Seo, J.J.[Jeong-Jik], Kim, H.I.[Hyung-Il], de Neve, W.[Wesley], Ro, Y.M.[Yong Man],
Effective and efficient human action recognition using dynamic frame skipping and trajectory rejection,
IVC(58), No. 1, 2017, pp. 76-85.
Elsevier DOI 1703
Frame skipping 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

Seo, J.J.[Jeong-Jik], Son, J.[Jisoo], Kim, H.I.[Hyung-Il], de Neve, W.[Wesley], Ro, Y.M.[Yong Man],
Efficient and effective human action recognition in video through motion boundary description with a compact set of trajectories,
FG15(1-6)
IEEE DOI 1508
feature extraction 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

Matsui, K.[Kenji], Tamaki, T.[Toru], Raytchev, B.[Bisser], Kaneda, K.[Kazufumi],
Trajectory-Set Feature for Action Recognition,
IEICE(E100-D), No. 8, August 2017, pp. 1922-1924.
WWW Link. 1708
BibRef

Raytchev, B.[Bisser], Kawamoto, H., Tamaki, T.[Toru], Kaneda, K.[Kazufumi],
Higher-level representation of local spatio-temporal features for human action recognition using Subspace Matching Kernels,
ICPR16(3862-3867)
IEEE DOI 1705
Feature extraction, Histograms, Kernel, Manifolds, Measurement, Pattern recognition, Videos BibRef

Raytchev, B.[Bisser], Shigenaka, R.[Ryosuke], Tamaki, T.[Toru], Kaneda, K.[Kazufumi],
Action recognition by orthogonalized subspaces of local spatio-temporal features,
ICIP13(4387-4391)
IEEE DOI 1402
Action Recognition BibRef

Sultani, W.[Waqas], Zhang, D.[Dong], Shah, M.[Mubarak],
Unsupervised action proposal ranking through proposal recombination,
CVIU(161), No. 1, 2017, pp. 42-50.
Elsevier DOI 1708
Action proposal ranking BibRef

Sultani, W.[Waqas], Shah, M.[Mubarak],
Automatic action annotation in weakly labeled videos,
CVIU(161), No. 1, 2017, pp. 77-86.
Elsevier DOI 1708
BibRef
And:
What If We Do Not have Multiple Videos of the Same Action? Video Action Localization Using Web Images,
CVPR16(1077-1085)
IEEE DOI 1612
Weakly supervised. 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

Du, W.B.[Wen-Bin], Wang, Y.[Yali], Qiao, Y.[Yu],
Recurrent Spatial-Temporal Attention Network for Action Recognition in Videos,
IP(27), No. 3, March 2018, pp. 1347-1360.
IEEE DOI 1801
BibRef
And:
RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos,
ICCV17(3745-3754)
IEEE DOI 1802
Computer vision, Feature extraction, Image recognition, Optical imaging, Recurrent neural networks, spatial-temporal attention. image motion analysis, pose estimation, video signal processing, RNNs, RPAN, 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

Yeung, S.[Serena], Russakovsky, O.[Olga], Jin, N.[Ning], Andriluka, M.[Mykhaylo], Mori, G.[Greg], Fei-Fei, L.[Li],
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos,
IJCV(126), No. 2-4, April 2018, pp. 375-389.
Springer DOI 1804
Dense labels -- every frame. LSTM networks. 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

Varol, G.[Gül], Laptev, I.[Ivan], Schmid, C.[Cordelia],
Long-Term Temporal Convolutions for Action Recognition,
PAMI(40), No. 6, June 2018, pp. 1510-1517.
IEEE DOI 1805
Estimation, Network architecture, Neural networks, Optical filters, Optical imaging, Spatial resolution, Training, Action recognition, video analysis BibRef

El-Henawy, I.M.[Ibrahim M.], Ahmed, K.[Kareem], Mahmoud, H.A.[Hamdi A.],
Action recognition using fast HOG3D of integral videos and Smith-Waterman partial matching,
IET-IPR(12), No. 6, June 2018, pp. 896-908.
DOI Link 1805
BibRef
Earlier: A2, A1, A3:
Action recognition technique based on fast HOG3D of integral foreground snippets and random forest,
ISCV17(1-7)
IEEE DOI 1710
image recognition, image representation, input video file, random forest, videos representation, Trajectory, Action recognition, HOG3D, Random Forest, gesture, spatio-temporal BibRef

Carmona, J.M.[Josep Maria], Climent, J.[Joan],
Human action recognition by means of subtensor projections and dense trajectories,
PR(81), 2018, pp. 443-455.
Elsevier DOI 1806
Action recognition, Subtensors, Dense trajectories, Keypoint descriptors, Temporal template BibRef

Phan, H.H.[Hai-Hong], Vu, N.S.[Ngoc-Son], Nguyen, V.L.[Vu-Lam], Quoy, M.[Mathias],
Action recognition based on motion of oriented magnitude patterns and feature selection,
IET-CV(12), No. 5, August 2018, pp. 735-743.
DOI Link 1807
BibRef
Earlier:
Motion of Oriented Magnitudes Patterns for Human Action Recognition,
ISVC16(II: 168-177).
Springer DOI 1701
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

Ghorbel, E.[Enjie], Boutteau, R.[Rémi], Boonaert, J.[Jacques], Savatier, X.[Xavier], Lecoeuche, S.[Stéphane],
Kinematic Spline Curves: A temporal invariant descriptor for fast action recognition,
IVC(77), 2018, pp. 60-71.
Elsevier DOI 1809
BibRef
Earlier:
A fast and accurate motion descriptor for human action recognition applications,
ICPR16(919-924)
IEEE DOI 1705
RBG-D cameras, Action recognition, Low computational latency, Temporal normalization. Acceleration, Interpolation, Kinematics, Kinetic energy, Skeleton, Splines (mathematics), Trajectory BibRef

Sabri, A.Q.M.[A.Q. Muhammad], Boonaert, J., Lecoeuche, S., Mouaddib, E.,
Human action classification using surf based spatio-temporal correlated descriptors,
ICIP12(1401-1404).
IEEE DOI 1302
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


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

Alwassel, H.[Humam], Heilbron, F.C.[Fabian Caba], Escorcia, V.[Victor], Ghanem, B.[Bernard],
Diagnosing Error in Temporal Action Detectors,
ECCV18(III: 264-280).
Springer DOI 1810
BibRef

Lee, M.G.[Myung-Gi], Lee, S.[Seungeui], Son, S.[Sungjoon], Park, G.[Gyutae], Kwak, N.[Nojun],
Motion Feature Network: Fixed Motion Filter for Action Recognition,
ECCV18(X: 392-408).
Springer DOI 1810
BibRef

Sun, C.[Chen], Shrivastava, A.[Abhinav], Vondrick, C.[Carl], Murphy, K.[Kevin], Sukthankar, R.[Rahul], Schmid, C.[Cordelia],
Actor-Centric Relation Network,
ECCV18(XI: 335-351).
Springer DOI 1810
BibRef

Liu, C., Xu, X., Zhang, Y.,
Temporal Attention Network for Action Proposal,
ICIP18(2281-2285)
IEEE DOI 1809
Temporal action proposal, temporal attention, untrimmed video analysis, neural network BibRef

Kwon, O.C., Kim, J., Yoo, C.D.,
Action Recognition: First-and Second-Order 3D Feature in Bi-Directional Attention Network,
ICIP18(3453-3457)
IEEE DOI 1809
Bidirectional control, Computer architecture, Convolutional neural networks, Visualization, Feeds, spatio-temporal bi-directional LSTM Attention 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

Xu, M., Sharghi, A., Chen, X., Crandall, D.J.,
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition,
WACV18(1607-1615)
IEEE DOI 1806
computer vision, feature extraction, image motion analysis, image recognition, image sequences, spatiotemporal phenomena, Videos BibRef

Yu, R., Wang, H., Davis, L.S.,
ReMotENet: Efficient Relevant Motion Event Detection for Large-Scale Home Surveillance Videos,
WACV18(1642-1651)
IEEE DOI 1806
image motion analysis, learning (artificial intelligence), neural nets, object detection, video surveillance, 3D ConvNets, Videos BibRef

Ng, J.Y.H., Davis, L.S.,
Temporal Difference Networks for Video Action Recognition,
WACV18(1587-1596)
IEEE DOI 1806
feedforward neural nets, image classification, image motion analysis, image recognition, image representation, BibRef

Ng, J.Y.H., Choi, J., Neumann, J., Davis, L.S.[Larry S.],
ActionFlowNet: Learning Motion Representation for Action Recognition,
WACV18(1616-1624)
IEEE DOI 1806
image motion analysis, image recognition, image representation, image sequences, learning (artificial intelligence), neural nets, Task analysis BibRef

Zhang, T.Y.[Tian-Yi], Niu, L.[Li], Cai, J.F.[Jian-Fei], Kot, A.C.[Alex C.],
Action proposals using hierarchical clustering of super-trajectories,
VCIP17(1-4)
IEEE DOI 1804
gesture recognition, sport, unsupervised learning, video signal processing, action localization task, Trajectory Grouping 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

Fang, H., Thiyagalingam, J., Bessis, N., Edirisinghe, E.,
Fast and reliable human action recognition in video sequences by sequential analysis,
ICIP17(3973-3977)
IEEE DOI 1803
Feature extraction, Reliability, Sequential analysis, Streaming media, Task analysis, Training, Video sequences, sequential probability ratio test(SPRT) 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

Lin, T., Zhao, X., Fan, Z.,
Temporal action localization with two-stream segment-based RNN,
ICIP17(3400-3404)
IEEE DOI 1803
Bidirectional control, Computer architecture, Feature extraction, Recurrent neural networks, Task analysis, Training, Videos, LSTM, RNN, Two-stream ConvNet 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

Liu, Z., Tian, Y., Wang, Z.,
Improving human action recognitionby temporal attention,
ICIP17(870-874)
IEEE DOI 1803
Adaptation models, Computer architecture, Feature extraction, Optical imaging, Recurrent neural networks, Training, Videos, temporal attention BibRef

Alwando, E.H.P., Chen, Y.T., Fang, W.H.,
Multiple path search for action tube detection in videos,
ICIP17(4232-4236)
IEEE DOI 1803
Complexity theory, Electron tubes, Message passing, Proposals, Radio frequency, Search problems, Videos, Action localization, object detection BibRef

Xiao, X., Hu, H., Wang, W.,
Trajectories-based motion neighborhood feature for human action recognition,
ICIP17(4147-4151)
IEEE DOI 1803
Computer vision, Handheld computers, Indexes, Pattern recognition, Support vector machines, Trajectory, linear SVM BibRef

Pu, J., Matsui, Y., Yang, F., Satoh, S.,
Energy based fast event retrieval in video with temporal match kernel,
ICIP17(885-889)
IEEE DOI 1803
Event retrieval, temporal match kernel BibRef

Hou, R.[Rui], Chen, C.[Chen], Shah, M.[Mubarak],
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos,
ICCV17(5823-5832)
IEEE DOI 1802
convolution, feature extraction, image classification, image motion analysis, image recognition, Videos 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

Kalogeiton, V.[Vicky], Weinzaepfel, P.[Philippe], Ferrari, V.[Vittorio], Schmid, C.[Cordelia],
Action Tubelet Detector for Spatio-Temporal Action Localization,
ICCV17(4415-4423)
IEEE DOI 1802
convolution, feature extraction, image sequences, object detection, regression analysis, video signal processing, ACT-detector, Videos 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

Lee, H.Y.[Hsin-Ying], Huang, J.B.[Jia-Bin], Singh, M.[Maneesh], Yang, M.H.[Ming-Hsuan],
Unsupervised Representation Learning by Sorting Sequences,
ICCV17(667-676)
IEEE DOI 1802
Learn the temporal sequence. feature extraction, image classification, image representation, image sequences, neural nets, object detection, sorting, Visualization 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

Buch, S., Escorcia, V., Shen, C., Ghanem, B., Niebles, J.C.,
SST: Single-Stream Temporal Action Proposals,
CVPR17(6373-6382)
IEEE DOI 1711
Computational modeling, Computer architecture, Proposals, Training, Video sequences, Visualization BibRef

Yuan, Z., Stroud, J.C., Lu, T., Deng, J.,
Temporal Action Localization by Structured Maximal Sums,
CVPR17(3215-3223)
IEEE DOI 1711
Benchmark testing, Computational efficiency, Computational modeling, Feature extraction, Neural networks, Videos BibRef

Wang, H., Wang, L.,
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks,
CVPR17(3633-3642)
IEEE DOI 1711
Computer architecture, Hidden Markov models, Logic gates, Recurrent neural networks, Skeleton, Videos BibRef

Liu, S.[Si], Wang, C.H.[Chang-Hu], Qian, R.H.[Rui-He], Yu, H.[Han], Bao, R.[Renda], Sun, Y.[Yao],
Surveillance Video Parsing with Single Frame Supervision,
CVPR17(1013-1021)
IEEE DOI 1711
Estimation, Image segmentation, Optical imaging, Semantics, Surveillance, Testing, Training One labelled frame in the video. BibRef

Sigurdsson, G.A., Divvala, S., Farhadi, A., Gupta, A.,
Asynchronous Temporal Fields for Action Recognition,
CVPR17(5650-5659)
IEEE DOI 1711
Cognition, Hidden Markov models, Semantics, Stochastic processes, Training, Videos 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

Fotiadou, E.[Eftychia], Panagakis, Y.[Yiannis], Pantic, M.[Maja],
Temporal Archetypal Analysis for Action Segmentation,
FG17(490-496)
IEEE DOI 1707
Convergence, Data mining, Feature extraction, Optimization, Symmetric matrices, Time series analysis, Visualization 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

Lan, Z.Z.[Zhen-Zhong], Yu, S.I.[Shoou-I], Yao, D.Z.[De-Zhong], Lin, M.[Ming], Raj, B.[Bhiksha], Hauptmann, A.G.[Alexander G.],
The Best of Both Worlds: Combining Data-Independent and Data-Driven Approaches for Action Recognition,
Robust16(1196-1205)
IEEE DOI 1612
Video features. 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

Yuan, J.[Jun], Ni, B.B.[Bing-Bing], Yang, X.K.[Xiao-Kang], Kassim, A.A.[Ashraf A.],
Temporal Action Localization with Pyramid of Score Distribution Features,
CVPR16(3093-3102)
IEEE DOI 1612
BibRef

Alwassel, H.[Humam], Heilbron, F.C.[Fabian Caba], Ghanem, B.[Bernard],
Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization,
ECCV18(IX: 253-269).
Springer DOI 1810
BibRef

Heilbron, F.C.[Fabian Caba], Niebles, J.C.[Juan Carlos], Ghanem, B.[Bernard],
Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos,
CVPR16(1914-1923)
IEEE DOI 1612
BibRef

Li, Y.W.[Ying-Wei], Li, Y.[Yi], Vasconcelos, N.M.[Nuno M.],
RESOUND: Towards Action Recognition Without Representation Bias,
ECCV18(VI: 520-535).
Springer DOI 1810
BibRef

Li, Y.W.[Ying-Wei], Li, W.X.[Wei-Xin], Mahadevan, V.[Vijay], Vasconcelos, N.M.[Nuno M.],
VLAD3: Encoding Dynamics of Deep Features for Action Recognition,
CVPR16(1951-1960)
IEEE DOI 1612
BibRef

de Souza, C.R.[César Roberto], Gaidon, A.[Adrien], Vig, E.[Eleonora], López, A.M.[Antonio Manuel],
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition,
ECCV16(VII: 697-716).
Springer DOI 1611
BibRef

Wang, L.M.[Li-Min], Xiong, Y.J.[Yuan-Jun], Lin, D.[Dahua], Van Gool, L.J.[Luc J.],
UntrimmedNets for Weakly Supervised Action Recognition and Detection,
CVPR17(6402-6411)
IEEE DOI 1711
Adaptation models, Feature extraction, Motion pictures, Proposals, Training, Videos, Visualization BibRef

Wang, L.M.[Li-Min], Xiong, Y.J.[Yuan-Jun], Wang, Z.[Zhe], Qiao, Y.[Yu], Lin, D.[Dahua], Tang, X.[Xiaoou], Van Gool, L.J.[Luc J.],
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition,
ECCV16(VIII: 20-36).
Springer DOI 1611
BibRef

Kim, T.S., Reiter, A.[Austin],
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks,
MotionRep17(1623-1631)
IEEE DOI 1709
Activity recognition, Computational modeling, Feature extraction, Skeleton, Solid modeling. BibRef

Leyva, R.[Roberto], Sanchez, V.[Victor], Li, C.T.[Chang-Tsun],
Fast Binary-Based Video Descriptors for Action Recognition,
DICTA16(1-8)
IEEE DOI 1701
BibRef
Earlier:
A fast binary pair-based video descriptor for action recognition,
ICIP16(4185-4189)
IEEE DOI 1610
Detectors. 3D Binary Pair Differences (3DBPD) for video action recognition. BibRef

Chen, Q.Q., Liu, F., Li, X., Liu, B.D., Zhang, Y.J.,
Saliency-context two-stream convnets for action recognition,
ICIP16(3076-3080)
IEEE DOI 1610
Adaptive optics BibRef

Belhadj, L.C., Mignotte, M.,
Spatio-temporal fastmap-based mapping for human action recognition,
ICIP16(3046-3050)
IEEE DOI 1610
Correlation BibRef

Li, Z., Wang, W., Li, N., Wang, J.,
Tube ConvNets: Better exploiting motion for action recognition,
ICIP16(3056-3060)
IEEE DOI 1610
Clustering algorithms 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

Wu, Z.X.[Zu-Xuan], Fu, Y.W.[Yan-Wei], Jiang, Y.G.[Yu-Gang], Sigal, L.[Leonid],
Harnessing Object and Scene Semantics for Large-Scale Video Understanding,
CVPR16(3112-3121)
IEEE DOI 1612
BibRef

Feichtenhofer, C.[Christoph], Pinz, A.[Axel], Zisserman, A.,
Convolutional Two-Stream Network Fusion for Video Action Recognition,
CVPR16(1933-1941)
IEEE DOI 1612
BibRef

Feichtenhofer, C.[Christoph], Pinz, A.[Axel], Wildes, R.P.[Richard P.],
Dynamically encoded actions based on spacetime saliency,
CVPR15(2755-2764)
IEEE DOI 1510
BibRef

Jia, C.C.[Cheng-Cheng], Pang, W.[Wei], Fu, Y.[Yun],
Mode-Driven Volume Analysis Based on Correlation of Time Series,
VECTaR14(818-833).
Springer DOI 1504
BibRef

Sun, X.[Xin], Huang, D.[Di], Wang, Y.H.[Yun-Hong], Qin, J.[Jie],
Action recognition based on kinematic representation of video data,
ICIP14(1530-1534)
IEEE DOI 1502
Acceleration BibRef

Hara, K.[Kensho], Hirayama, T.[Takatsugu], Mase, K.[Kenji],
Trend-sensitive hough forests for action detection,
ICIP14(1475-1479)
IEEE DOI 1502
Accuracy 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

Liu, M.[Mengyuan], Liu, H.[Hong], Sun, Q.[Qianru],
Action classification by exploring directional co-occurrence of weighted stips,
ICIP14(1460-1464)
IEEE DOI 1502
Accuracy BibRef

Han, T.T.[Ting-Ting], Yao, H.X.[Hong-Xun], Zhang, Y.H.[Yan-Hao], Xu, P.F.[Peng-Fei],
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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:Nov 17, 2018 at 09:12:27