B. Amor, J. Su, and A. Srivastava, Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.1, pp.1-13, 2016.
DOI : 10.1109/TPAMI.2015.2439257

P. Bilinski, E. Corvee, S. Bak, and F. Bremond, Relative dense tracklets for human action recognition, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013.
DOI : 10.1109/FG.2013.6553699
URL : https://hal.archives-ouvertes.fr/hal-00806321

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

N. Dalal, B. Triggs, and C. Schmid, Human Detection Using Oriented Histograms of Flow and Appearance, ECCV, 2006.
DOI : 10.1023/A:1008162616689
URL : https://hal.archives-ouvertes.fr/inria-00548587

Y. Du, W. Wang, and L. Wang, Hierarchical recurrent neural network for skeleton based action recognition, CVPR, 2015.

J. Hu, W. Zheng, J. Lai, and J. Zhang, Jointly learning heterogeneous features for RGB-D activity recognition, CVPR, 2015.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar et al., Large-Scale Video Classification with Convolutional Neural Networks, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.223

Y. Kong and Y. Fu, Bilinear heterogeneous information machine for RGB-D action recognition, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298708

H. Koppula and A. Saxena, Learning spatio-temporal structure from rgb-d videos for human activity detection and anticipation, ICML, 2013.

H. S. Koppula, R. Gupta, and A. Saxena, Learning human activities and object affordances from RGB-D videos, The International Journal of Robotics Research, vol.32, issue.8, pp.951-970, 2013.
DOI : 10.1177/0278364913478446

J. Krapac, J. Verbeek, and F. Jurie, Modeling spatial layout with fisher vectors for image categorization, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126406
URL : https://hal.archives-ouvertes.fr/inria-00612277

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

I. Laptev and T. Lindeberg, Space-time interest points, ICCV, 2003.
DOI : 10.1109/iccv.2003.1238378
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.4359

S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.68
URL : https://hal.archives-ouvertes.fr/inria-00548585

L. Liu and L. Shao, Learning discriminative representations from rgb-d video data A decision forest based feature selection framework for action recognition from rgb-depth cameras, IJCAI, 2013. [16] ICIAR, 2013.

B. Ni, P. Moulin, and S. Yan, Order-Preserving Sparse Coding for Sequence Classification, ECCV, 2012.
DOI : 10.1007/978-3-642-33709-3_13

O. Oreifej and Z. Liu, HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.98

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Liu, J. Sanchez, and H. Poirier. Large-Scale Image Retrieval with Compressed Fisher Vectors CVPR, pp.2825-2830, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00650905

F. Perronnin, J. Sanchez, and T. Mensink, Improving the Fisher Kernel for Large-Scale Image Classification, ECCV, 2010.
DOI : 10.1007/978-3-642-15561-1_11
URL : https://hal.archives-ouvertes.fr/inria-00548630

H. Rahmani, A. Mahmood, D. Q. Huynh, and A. Mian, HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition, ECCV, 2014.
DOI : 10.1007/978-3-319-10605-2_48

L. Rybok, B. Schauerte, Z. Halah, and R. Stiefelhagen, “Important stuff, everywhere!” Activity recognition with salient proto-objects as context, IEEE Winter Conference on Applications of Computer Vision, 2014.
DOI : 10.1109/WACV.2014.6836041

L. Seidenari, V. Varano, S. Berretti, A. D. Bimbo, and P. Pala, Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2013.
DOI : 10.1109/CVPRW.2013.77

A. Shahroudy, G. Wang, and T. Ng, Multi-modal feature fusion for action recognition in RGB-D sequences, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2014.
DOI : 10.1109/ISCCSP.2014.6877819

L. Spinello and K. O. Arras, People detection in RGB-D data, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011.
DOI : 10.1109/IROS.2011.6095074

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

H. Wang, A. Kläser, C. Schmid, and C. Liu, Action recognition by dense trajectories, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995407
URL : https://hal.archives-ouvertes.fr/inria-00583818

L. Wang, Y. Qiao, and X. Tang, Action recognition with trajectory-pooled deep-convolutional descriptors, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7299059

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

L. Xia and J. Aggarwal, Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.365

X. Yang and Y. Tian, Super Normal Vector for Activity Recognition Using Depth Sequences, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.108

Y. Zhu, W. Chen, and G. Guo, Evaluating spatiotemporal interest point features for depth-based action recognition, Image and Vision Computing, vol.32, issue.8, pp.453-464, 2014.
DOI : 10.1016/j.imavis.2014.04.005