B. 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

L. Breiman, Random Forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

A. A. Chaaraoui, J. R. Padilla-lopez, and F. Florez-revuelta, Fusion of Skeletal and Silhouette-Based Features for Human Action Recognition with RGB-D Devices, 2013 IEEE International Conference on Computer Vision Workshops, pp.91-97, 2013.
DOI : 10.1109/ICCVW.2013.19

C. Chen, R. Jafari, and N. Kehtarnavaz, Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns, 2015 IEEE Winter Conference on Applications of Computer Vision, pp.1092-1099, 2015.
DOI : 10.1109/WACV.2015.150

C. Chen, K. Liu, and N. Kehtarnavaz, Real-time human action recognition based on depth motion maps, Journal of Real-Time Image Processing, vol.32, issue.1, pp.155-163, 2016.
DOI : 10.1023/A:1007469218079

Y. Du, W. Wang, and L. Wang, Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition, pp.1110-1118, 2015.

G. Evangelidis, G. Singh, and R. Horaud, Skeletal Quads: Human Action Recognition Using Joint Quadruples, 2014 22nd International Conference on Pattern Recognition, pp.4513-4518, 2014.
DOI : 10.1109/ICPR.2014.772

URL : https://hal.archives-ouvertes.fr/hal-00989725

R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin, LIBLINEAR: A Library for Large Linear Classification, Journal of Machine Learning Research, vol.9, pp.1871-1874, 2008.

M. A. Gowayyed, M. Torki, M. E. Hussein, and M. El-saban, Histogram of Oriented Displacements (HOD): Describing Trajectories of Human Joints for Action Recognition, pp.1351-1357, 2013.

G. B. Huang and L. Chen, Convex incremental extreme learning machine, Neurocomputing, vol.70, issue.16-18, pp.16-18, 2007.
DOI : 10.1016/j.neucom.2007.02.009

G. B. Huang and L. Chen, Enhanced random search based incremental extreme learning machine, Neurocomputing, vol.71, issue.16-18, pp.16-18, 2008.
DOI : 10.1016/j.neucom.2007.10.008

G. B. Huang, H. Zhou, X. Ding, and R. Zhang, Extreme Learning Machine for Regression and Multiclass Classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.2, pp.513-529, 2012.
DOI : 10.1109/TSMCB.2011.2168604

G. B. Huang, Q. Y. Zhu, and C. K. Siew, Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks, IEEE International Joint Conference on Neural Networks, pp.985-990, 2004.

A. Kurakin, Z. Zhang, and Z. Liu, A Read-Time System for Dynamic Hand Gesture Recognition with A Depth Sensor, pp.1975-1979, 2012.

I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, Learning realistic human actions from movies, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587756

URL : https://hal.archives-ouvertes.fr/inria-00548659

W. Li, Z. Zhang, and Z. Liu, Expandable Data-Driven Graphical Modeling of Human Actions Based on Salient Postures, IEEE Transactions on Circuits and Systems for Video Technology, vol.18, issue.11, pp.1499-1510, 2008.

W. Li, Z. Zhang, and Z. Liu, Action recognition based on a bag of 3D points, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Workshops, pp.9-14, 2010.
DOI : 10.1109/CVPRW.2010.5543273

C. Liang, E. Chen, L. Qi, and L. Guan, Improving Action Recognition Using Collaborative Representation of Local Depth Map Feature, IEEE Signal Processing Letters, vol.23, issue.9, pp.1241-1245, 2016.
DOI : 10.1109/LSP.2016.2592419

L. Liu, C. Shen, L. Wang, A. Van-den-hengel, and C. Wang, Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors, pp.1143-1151, 2014.

J. Luo, W. Wang, and H. Qi, Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps, 2013 IEEE International Conference on Computer Vision, pp.1809-1816, 2013.
DOI : 10.1109/ICCV.2013.227

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1615-1630, 2005.
DOI : 10.1109/TPAMI.2005.188

URL : https://hal.archives-ouvertes.fr/inria-00548529

M. Müller, Information Retrieval for Music and Motion, Inc, 2007.
DOI : 10.1007/978-3-540-74048-3

R. M. Murray, S. S. Sastry, and L. Zexiang, A Mathematical Introduction to Robotic Manipulation, 1994.

C. W. Ngo, T. C. Pong, and R. T. Chin, Detection of gradual transitions through temporal slice analysis, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pp.36-41, 1999.
DOI : 10.1109/CVPR.1999.786914

T. Ojala, M. Pietikainen, and D. Harwood, Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, Proceedings of 12th International Conference on Pattern Recognition, pp.582-585, 1994.
DOI : 10.1109/ICPR.1994.576366

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, pp.716-723, 2013.
DOI : 10.1109/CVPR.2013.98

J. R. Padilla-lópez, A. A. Chaaraoui, and F. Flórez-revuelta, A Discussion on the Validation Tests Employed to Compare Human Action Recognition Methods Using the MSR Action3D Dataset, p.7390, 2014.

H. Rahmani and A. Mian, 3D Action Recognition from Novel Viewpoints, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1506-1515, 2016.
DOI : 10.1109/CVPR.2016.167

J. Sanchez, F. Perronnin, T. Mensink, and J. Verbeek, Image Classification with the Fisher Vector: Theory and Practice, International Journal of Computer Vision, vol.73, issue.2, pp.222-245, 2013.
DOI : 10.1007/s11263-006-9794-4

URL : https://hal.archives-ouvertes.fr/hal-00779493

J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, vol.61, pp.85-117, 2015.
DOI : 10.1016/j.neunet.2014.09.003

Y. Song, S. Liu, and J. Tang, Describing Trajectory of Surface Patch for Human Action Recognition on RGB and Depth Videos, IEEE Signal Processing Letters, vol.22, issue.4, pp.426-429, 2015.
DOI : 10.1109/LSP.2014.2361901

A. Vedaldi and B. Fulkerson, Vlfeat, Proceedings of the international conference on Multimedia, MM '10, pp.1469-1472, 2010.
DOI : 10.1145/1873951.1874249

R. Vemulapalli, F. Arrate, and R. Chellappa, Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.588-595, 2014.
DOI : 10.1109/CVPR.2014.82

C. Wang, Y. Wang, and A. L. Yuille, An Approach to Pose-Based Action Recognition, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.915-922, 2013.
DOI : 10.1109/CVPR.2013.123

J. Wang, Z. Liu, J. Chorowski, Z. Chen, and Y. Wu, Robust 3D Action Recognition with Random Occupancy Patterns, pp.872-885, 2012.
DOI : 10.1007/978-3-642-33709-3_62

J. Wang, Z. Liu, Y. Wu, and J. Yuan, Mining actionlet ensemble for action recognition with depth cameras, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.1290-1297, 2012.
DOI : 10.1109/CVPR.2012.6247813

X. Yang and Y. Tian, Super Normal Vector for Activity Recognition Using Depth Sequences, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.804-811, 2014.
DOI : 10.1109/CVPR.2014.108

X. Yang and Y. L. Tian, EigenJoints-based Action Recognition Using Naive-Bayes-Nearest- Neighbor, pp.14-19, 2012.

X. Yang, C. Zhang, and Y. Tian, Recognizing actions using depth motion maps-based histograms of oriented gradients, Proceedings of the 20th ACM international conference on Multimedia, MM '12, pp.1057-1060, 2012.
DOI : 10.1145/2393347.2396382

B. Zhang, Y. Gao, S. Zhao, and J. Liu, Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor, IEEE Transactions on Image Processing, vol.19, issue.2, pp.533-544, 2010.
DOI : 10.1109/TIP.2009.2035882

G. Zhao and M. Pietikainen, Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.6, pp.915-928, 2007.
DOI : 10.1109/TPAMI.2007.1110

Y. Zhu, W. Chen, and G. Guo, Fusing Spatiotemporal Features and Joints for 3D Action Recognition, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.486-491, 2013.
DOI : 10.1109/CVPRW.2013.78