H. Wang, A. Kläser, C. Schmid, and C. Liu, Action recognition by dense trajectories, The 24th IEEE Conference on Computer Vision and Pattern Recognition, pp.3169-3176, 2011.
DOI : 10.1109/cvpr.2011.5995407
URL : https://hal.archives-ouvertes.fr/inria-00583818

G. Chéron, I. Laptev, and C. Schmid, P-CNN: pose-based CNN features for action recognition, ICCV 2015, pp.3218-3226, 2015.

D. Tran, L. D. Bourdev, R. Fergus, L. Torresani, and M. Paluri, Learning spatiotemporal features with 3d convolutional networks, ICCV, pp.4489-4497, 2015.
DOI : 10.1109/iccv.2015.510
URL : http://arxiv.org/pdf/1412.0767

O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce, Automatic annotation of human actions in video, ICCV
DOI : 10.1109/iccv.2009.5459279
URL : http://www.irisa.fr/vista/Papers/2009_iccv_duchenne.pdf

M. Hoai, Z. Lan, F. De-la, and T. , Joint segmentation and classification of human actions in video, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp.3265-3272, 2011.
DOI : 10.1109/cvpr.2011.5995470
URL : http://www.andrew.cmu.edu/user/minhhoan/papers/SegReg_CVPR11.pdf

I. Laptev and P. Perez, Retrieving actions in movies, 2007 IEEE 11th ICCV, pp.1-8, 2007.
DOI : 10.1109/iccv.2007.4409105
URL : http://www.irisa.fr/vista/Papers/2007_iccv_laptev.pdf

Y. Jiang, J. Liu, A. Zamir, G. Toderici, I. Laptev et al., THUMOS challenge: Action recognition with a large number of classes, 2014.

B. G. Fabian-caba-heilbron, V. Escorcia, and J. C. Niebles, Activitynet: A large-scale video benchmark for human activity understanding, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.961-970, 2015.

G. Vaquette, A. Orcesi, L. Lucat, and C. Achard, The daily home life activity dataset: A high semantic activity dataset for online recognition, 2017 12th IEEE International Conference on Automatic Face Gesture Recognition, pp.497-504, 2017.
DOI : 10.1109/fg.2017.67
URL : https://hal.archives-ouvertes.fr/cea-01841019

A. Karakostas, A. Briassouli, K. Avgerinakis, I. Kompatsiaris, and M. Tsolaki, The dem@ care experiments and datasets: a technical report, 2016.

M. Koperski, Human action recognition in videos with local representation, 2017.
URL : https://hal.archives-ouvertes.fr/tel-01648968

A. Sharaf, M. Torki, M. E. Hussein, and M. El-saban, Real-time multi-scale action detection from 3d skeleton data, Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pp.998-1005, 2015.
DOI : 10.1109/wacv.2015.138

D. Oneata, J. Verbeek, and C. Schmid, , 2014.

T. Lin, X. Zhao, and Z. Shou, Single shot temporal action detection, Proceedings of the 2017 ACM on Multimedia Conference, pp.988-996, 2017.

S. Buch, V. Escorcia, B. Ghanem, L. Fei-fei, and J. Niebles, End-toend, single-stream temporal action detection in untrimmed videos, Proceedings of the British Machine Vision Conference (BMVC, 2017.

H. Xu, A. Das, and K. Saenko, R-c3d: Region convolutional 3d network for temporal activity detection, 2017.

K. Avgerinakis, A. Briassouli, and I. Kompatsiaris, Activity detection and recognition of daily living events, Health Monitoring and Personalized Feedback using Multimedia Data, pp.139-160, 2015.

Y. Li, C. Lan, J. Xing, W. Zeng, C. Yuan et al., Online human action detection using joint classification-regression recurrent neural networks, ECCV, pp.203-220, 2016.

M. Meshry, M. E. Hussein, and M. Torki, Linear-time online action detection from 3d skeletal data using bags of gesturelets, 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, pp.1-9, 2016.

C. Chen and K. Grauman, Efficient activity detection in untrimmed video with max-subgraph search, IEEE Trans. Pattern Anal. Mach. Intell, vol.39, issue.5, pp.908-921, 2017.

A. Chan-hon-tong, C. Achard, and L. Lucat, Deeply optimized hough transform: Application to action segmentation, Image Analysis and Processing-ICIAP 2013-17th International Conference, pp.51-60, 2013.
URL : https://hal.archives-ouvertes.fr/cea-01813733

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, CVPR 2005. IEEE Computer Society Conference on, vol.1, pp.886-893, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed et al., SSD: Single shot multibox detector, ECCV, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, CVPR, pp.770-778, 2016.