J. K. Aggarwal and M. S. Ryoo, Human activity analysis, ACM Computing Surveys, vol.43, issue.3, pp.43-59, 2011.
DOI : 10.1145/1922649.1922653

S. Vishwakarma and A. Agrawal, A survey on activity recognition and behavior understanding in video surveillance', The Visual Computer, pp.29-983, 2013.

P. V. Borges, N. Conci, and A. Cavallaro, Video-Based Human Behavior Understanding: A Survey, IEEE transactions on circuits and systems for video technology, pp.23-1993, 2013.
DOI : 10.1109/TCSVT.2013.2270402

URL : http://www.eecs.qmul.ac.uk/~andrea/papers/2013_TCSVT_BehaviourUnderstanding_Borges_Conci_Cavallaro.pdf

M. Vrigkas, C. Nikou, and I. A. Kakadiaris, A review of human activity recognition methods', Frontiers in Robotics and AI, p.28, 2015.

A. A. Liu, N. Xu, Y. T. Su, H. Lin, T. Hao et al., Single/multi-view human action recognition via regularized multi-task learning, Neurocomputing, vol.151, pp.2015-151
DOI : 10.1016/j.neucom.2014.04.090

C. Y. Chen and K. Grauman, Efficient activity detection with max-subgraph search, Computer Vision and Pattern Recognition (CVPR), pp.1274-1281, 2012.

S. Samanta and B. Chanda, Space-time Facet Model for Human Activity Classification, IEEE Transactions on Multimedia, vol.2014, issue.166, pp.1525-1535
DOI : 10.1109/TMM.2014.2326734

H. Riemenschneider, M. Donoser, and H. Bischof, Bag of Optical Flow Volumes for Image Sequence Recognition, Procedings of the British Machine Vision Conference 2009, pp.1-11, 2009.
DOI : 10.5244/C.23.28

X. Cao, H. Zhang, C. Deng, Q. Liu, and H. Liu, Action recognition using 3D DAISY descriptor' , Machine vision and applications, pp.159-171
DOI : 10.1007/s00138-013-0545-6

H. Wang, D. Oneata, J. Verbeek, and C. Schmid, A Robust and Efficient Video Representation for Action Recognition, International Journal of Computer Vision, vol.103, issue.1, pp.219-238, 2016.
DOI : 10.1109/ICCV.2013.442

URL : https://hal.archives-ouvertes.fr/hal-01145834

R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal, Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions' , In computer vision and pattern recognition, pp.1932-1939, 2009.

N. Ikizler, R. G. Cinbis, and P. Duygulu, Human action recognition with line and flow histograms' , In Pattern Recognition, 19th International Conference on, pp.1-4, 2008.

H. Tabia, M. Gouiffes, and L. Lacassagne, Motion histogram quantification for human action recognition, Pattern Recognition (ICPR), pp.2404-2407, 2012.

Z. Zhang, Y. Hu, S. Chan, and L. Chia, Motion Context: A New Representation for Human Action Recognition, European Conference on Computer Vision, pp.817-829, 2008.
DOI : 10.1007/978-3-540-88693-8_60

S. Ji, W. Xu, M. Yang, and K. Yu, 3D Convolutional Neural Networks for Human Action Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.1, pp.221-231, 2013.
DOI : 10.1109/TPAMI.2012.59

G. W. Taylor, R. Fergus, Y. Lecun, and C. Bregler, Convolutional Learning of Spatio-temporal Features, European conference on computer vision, pp.140-153, 2010.
DOI : 10.1007/978-3-642-15567-3_11

URL : http://yann.lecun.com/exdb/publis/pdf/taylor-eccv-10.pdf

M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt, Sequential Deep Learning for Human Action Recognition, International Workshop on Human Behavior Understanding, pp.29-39, 2011.
DOI : 10.1007/978-3-642-25446-8_4

URL : https://hal.archives-ouvertes.fr/hal-01354493

M. Vrigkas, V. Karavasilis, C. Nikou, and I. A. Kakadiaris, Matching mixtures of curves for human action recognition, Computer Vision and Image Understanding, vol.119, pp.119-146, 2014.
DOI : 10.1016/j.cviu.2013.11.007

Y. Ostrovsky, E. Meyers, S. Ganesh, U. Mathur, and P. Sinha, Visual Parsing After Recovery From Blindness, Psychological Science, vol.34, issue.6, pp.20-1484, 2009.
DOI : 10.1037/11496-005

A. Manzanera, Local Jet Feature Space Framework for Image Processing and Representation, 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems, pp.261-268, 2011.
DOI : 10.1109/SITIS.2011.49

URL : https://hal.archives-ouvertes.fr/hal-01119682

J. H. Van-hateren and D. L. Ruderman, Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in p0rimary visual cortex, Proceedings of the Royal Society of London B: Biological Sciences, pp.265-2315, 1412.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection', In Computer Vision and Pattern Recognition, pp.886-893, 2001.

J. Richefeu and A. Manzanera, A NEW HYBRID DIFFERENTIAL FILTER FOR MOTION DETECTION, Computer Vision and Graphics, pp.32-727, 2006.
DOI : 10.1007/1-4020-4179-9_105

URL : https://hal.archives-ouvertes.fr/hal-01222690

C. C. Chang and C. J. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, p.27, 2011.
DOI : 10.1145/1961189.1961199

L. Ballan, M. Bertini, D. Bimbo, A. Seidenari, L. Serra et al., Effective codebooks for human action categorization' In Computer Vision Workshops, pp.506-513, 2009.
DOI : 10.1109/iccvw.2009.5457658

R. Vezzani and R. Cucchiara, Video surveillance online repository (ViSOR), Proceedings of the 4th ACM Multimedia Systems Conference on, MMSys '13, pp.359-380, 2010.
DOI : 10.1145/2483977.2483987

M. Ryoo and J. Aggarwal, UT-Interaction Dataset, ICPR contest on Semantic Description of Human Activities, p.2010

G. Yu, J. Yuan, and Z. Liu, Propagative hough voting for human activity recognition, European Conference on Computer Vision, pp.693-706, 2012.
DOI : 10.1007/978-3-642-33712-3_50

URL : https://dr.ntu.edu.sg/bitstream/handle/10220/17873/Propagative%20Hough%20Voting%20for%20Human%20Activity%20Recognition.pdf%3Bjsessionid%3DC8EEB44665BD2AB963781E50CAC0271F?sequence%3D1

P. Scovanner, S. Ali, and M. Shah, A 3-dimensional sift descriptor and its application to action recognition, Proceedings of the 15th international conference on Multimedia , MULTIMEDIA '07, pp.357-360, 2007.
DOI : 10.1145/1291233.1291311

K. Nour-el-houda-slimani, Y. Benezeth, and F. Souami, Human interaction recognition based on the co-occurence of visual words, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.455-460, 2014.

S. Mukherjee, S. K. Biswas, and D. P. Mukherjee, Recognizing interaction between human performers using'key pose doublet, Proceedings of the 19th ACM international conference on Multimedia, pp.1329-1332, 2011.
DOI : 10.1145/2072298.2072006

M. S. Ryoo, Human activity prediction: Early recognition of ongoing activities from streaming videos, 2011 International Conference on Computer Vision, pp.1036-1043, 2011.
DOI : 10.1109/ICCV.2011.6126349

X. Ji, C. Wang, X. Zuo, and Y. Wang, Multiple Feature Voting based Human Interaction Recognition, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol.9, issue.1, pp.323-334
DOI : 10.14257/ijsip.2016.9.1.31

X. Ji, C. Wang, and Y. Li, A View-Invariant Action Recognition Based on Multi-View Space Hidden Markov Models, International Journal of Humanoid Robotics, vol.27, issue.01, pp.11-1450011, 2014.
DOI : 10.1016/j.cviu.2006.07.013

C. Schuldt, I. Laptev, and B. Caputo, Recognizing human actions: A local SVM approach', In Pattern Recognition, pp.32-36, 2003.
DOI : 10.1109/icpr.2004.1334462

URL : http://www.nada.kth.se/%7Ecaputo/publik/icpr04actions.pdf

P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie, Behavior recognition via sparse spatiotemporal features', In Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp.65-72, 2005.

M. S. Ryoo, B. Rothrock, and L. Matthies, Pooled motion features for first-person videos, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.896-904, 2015.
DOI : 10.1109/CVPR.2015.7298691