M. Abadi, TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.

F. Baradel, C. Wolf, and J. Mille, Human action recognition: Pose-based attention draws focus to hands, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp.604-613, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01575390

F. Baradel, C. Wolf, J. Mille, and G. W. Taylor, Glimpse clouds: Human activity recognition from unstructured feature points, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01713109

J. Carreira and A. Zisserman, Quo vadis, action recognition? a new model and the kinetics dataset, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4724-4733, 2017.

G. Cheron, I. Laptev, and C. Schmid, P-cnn: Pose-based cnn features for action recognition, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01187690

F. Chollet, , 2015.

S. Das, M. Koperski, F. Bremond, and G. Francesca, A Fusion of Appearance based CNNs and Temporal evolution of Skeleton with LSTM for Daily Living Action Recognition, 2018.

S. Das, M. Koperski, F. Bremond, and G. Francesca, Action recognition based on a mixture of rgb and depth based skeleton, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01639504

J. Donahue, A. Hendricks, L. Guadarrama, S. Rohrbach, M. Venugopalan et al., Long-term recurrent convolutional networks for visual recognition and description, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar et al., LargeScale Video Classification with Convolutional Neural Networks, 2014.

M. Koperski, Human Action Recognition in videos with Local Representation, 2017.
URL : https://hal.archives-ouvertes.fr/tel-01648968

M. Koperski and F. Bremond, Modeling spatial layout of features for real world scenario rgb-d action recognition, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01399037

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks. In: NIPS, 2012.

L. Van-der-maaten and G. E. Hinton, Visualizing data using t-sne, 2008.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. Shahroudy, J. Liu, T. T. Ng, and G. Wang, Ntu rgb+d: A large scale dataset for 3d human activity analysis, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

K. Simonyan and A. Zisserman, Two-stream convolutional networks for action recognition in videos, Advances in neural information processing systems, pp.568-576, 2014.

J. Sung, C. Ponce, B. Selman, and A. Saxena, Unstructured human activity detection from rgbd images, p.ICRA, 2012.

H. Wang, A. Kläser, C. Schmid, and C. L. Liu, Action Recognition by Dense Trajectories, IEEE Conference on Computer Vision & Pattern Recognition, pp.3169-3176, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00583818

H. Wang and C. Schmid, Action recognition with improved trajectories, IEEE International Conference on Computer Vision, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00873267

Y. Wu, Mining actionlet ensemble for action recognition with depth cameras, 2012.

P. Zhang, C. Lan, J. Xing, W. Zeng, J. Xue et al., View adaptive recurrent neural networks for high performance human action recognition from skeleton data, The IEEE International Conference on Computer Vision (ICCV), 2017.
DOI : 10.1109/iccv.2017.233

URL : http://arxiv.org/pdf/1703.08274

S. Zhang, X. Liu, and J. Xiao, On geometric features for skeleton-based action recognition using multilayer lstm networks, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp.148-157, 2017.
DOI : 10.1109/wacv.2017.24

M. Zolfaghari, G. L. Oliveira, N. Sedaghat, and T. Brox, Chained multi-stream networks exploiting pose, motion, and appearance for action classification and detection, 2017 IEEE International Conference on, pp.2923-2932, 2017.
DOI : 10.1109/iccv.2017.316

URL : http://arxiv.org/pdf/1704.00616