.. Segment-description, 95 5.3.1 Human motion description, Vocabulary of exemplar MUs . . . . . . . . . . . . . . . . . 100

M. F. Abdelkader, W. Abd-almageed, A. Srivastava, and R. Chellappa, Silhouette-based gesture and action recognition via modeling trajectories on Riemannian shape manifolds, Computer Vision and Image Understanding, vol.115, issue.3, pp.439-455, 2011.
DOI : 10.1016/j.cviu.2010.10.006

M. Aharon, M. Elad, and A. Bruckstein, $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

M. Ahmad and S. W. Lee, Human action recognition using shape and CLG-motion flow from multi-view image sequences, Pattern Recognition, vol.41, issue.7, pp.2237-2252, 2008.
DOI : 10.1016/j.patcog.2007.12.008

K. Aitpayev and J. Gaber, Collision Avatar (CA): Adding collision objects for human body in augmented reality using Kinect, 2012 6th International Conference on Application of Information and Communication Technologies (AICT), 2012.
DOI : 10.1109/ICAICT.2012.6398480

A. Kurakin, Z. Zhang, and Z. Liu, A real-time system for dynamic hand gesture recognition with a depth sensor, European Signal Processing Conference (EUSIPCO), 2012.

A. Srivastava, S. Joshi, W. Mio, and X. Liu, Statistical shape analysis: clustering, learning, and testing, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.4, pp.590-602, 2005.
DOI : 10.1109/TPAMI.2005.86

URL : http://calais.stat.fsu.edu/anuj/PDF-files/Papers/SrivastavaShapeAnalysis.pdf

H. H. Avilés-arriaga, L. E. Sucar-succar, C. E. Mendoza-durán, and L. A. Pineda-cortés, A comparison of dynamic naive bayesian classifiers and hidden markov models for gesture recognition, Journal of Applied Research and Technology, vol.9, issue.1, pp.81-102, 2011.

L. E. Baum, T. Petrie, G. Soules, and N. Weiss, A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains, The Annals of Mathematical Statistics, vol.41, issue.1, pp.164-171, 1970.
DOI : 10.1214/aoms/1177697196

R. Bellman and R. Kalaba, On adaptive control processes, IRE Transactions on Automatic Control, vol.4, issue.2, pp.1-9, 1959.
DOI : 10.1109/TAC.1959.1104847

R. E. Bellman and E. D. Stuart, Applied dynamic programming, RAND Corporation, 1962.
DOI : 10.1515/9781400874651

S. Berretti, A. D. Bimbo, and P. Pala, Superfaces: A Super-Resolution Model for 3D Faces, Proc. Work. on Non-Rigid Shape Analysis and Deformable Image Alignment, pp.73-82, 2012.
DOI : 10.1007/978-3-642-33863-2_8

W. Bian, D. Tao, and Y. Rui, Cross-Domain Human Action Recognition, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.2, pp.298-307, 2012.
DOI : 10.1109/TSMCB.2011.2166761

V. Bloom, V. Argyriou, and D. Makris, G3Di: A Gaming Interaction Dataset with a Real Time Detection and Evaluation Framework, Workshop of European Conference on Computer Vision (ECCV), 2014.
DOI : 10.1007/978-3-319-16178-5_49

E. Bondi, L. Seidenari, A. D. Bagdanov, and A. D. Bimbo, Real-time people counting from depth imagery of crowded environments, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2014.
DOI : 10.1109/AVSS.2014.6918691

I. Borg and P. J. Groenen, Modern Multidimensional Scaling: Theory and Applications, Journal of Educational Measurement, vol.40, issue.3, 2005.
DOI : 10.4135/9781412985130

A. A. Chaaraoui, P. Climent-perez, and F. Florez-revuelta, A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living, Expert Systems with Applications, vol.39, issue.12, pp.10873-10888
DOI : 10.1016/j.eswa.2012.03.005

C. Chang, B. Lange, M. Zhang, S. Koenig, P. Requejo et al., Towards Pervasive Physical Rehabilitation Using Microsoft Kinect, Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare
DOI : 10.4108/icst.pervasivehealth.2012.248714

X. Chen and M. Koskela, Skeleton-based action recognition with extreme learning machines, Neurocomputing, vol.149, pp.387-396
DOI : 10.1016/j.neucom.2013.10.046

J. W. Davis and A. F. Bobick, The representation and recognition of action using temporal templates, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997.

M. Devanne, H. Wannous, S. Berretti, P. Pala, M. Daoudi et al., 3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold, IEEE Transactions on Cybernetics, vol.45, issue.7, pp.1340-1352, 2014.
DOI : 10.1109/TCYB.2014.2350774

URL : https://hal.archives-ouvertes.fr/hal-01056397

A. Dubois and F. Charpillet, Human activities recognition with rgbdepth camera using hmm, International Conference of the IEEE Engineering in Medicine and Biology Society, 2013.
DOI : 10.1109/embc.2013.6610588

URL : https://hal.archives-ouvertes.fr/hal-00914319

V. Elangovan, V. K. Bandaru, and A. Shirkhodaie, Team activity analysis and recognition based on Kinect depth map and optical imagery techniques, Signal Processing, Sensor Fusion, and Target Recognition XXI, 2012.
DOI : 10.1117/12.919946

C. Ellis, S. Z. Masood, M. F. Tappen, J. J. La-viola-jr, and R. Sukthankar, Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition, International Journal of Computer Vision, vol.57, issue.2, pp.420-436, 2013.
DOI : 10.1023/B:VISI.0000013087.49260.fb

S. Fothergill, H. M. Mentis, P. Kohli, and S. Nowozin, Instructing people for training gestural interactive systems, Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, CHI '12, pp.1737-1746
DOI : 10.1145/2207676.2208303

URL : http://research.microsoft.com/en-us/um/people/pkohli/papers/fmkn_chi2012.pdf

S. Gasparrini, E. Cippitelli, S. Spinsante, and E. Gambi, A Depth-Based Fall Detection System Using a Kinect?? Sensor, Sensors, vol.6, issue.12, pp.2756-2775
DOI : 10.1049/iet-cvi.2011.0140

S. Hadfield and R. Bowden, Kinecting the dots: Particle based scene flow from depth sensors, 2011 International Conference on Computer Vision, pp.2290-2295, 2011.
DOI : 10.1109/ICCV.2011.6126509

N. Hadjiminas and C. H. Child, Be the controller: A kinect tool kit for video game control -recognition of human motion using skeletal relational angles, International Conference On Computer Games, Multimedia And Allied Technology (CGAT), 2012.

J. Han, L. Shao, D. Xu, and J. Shotton, Enhanced computer vision with microsoft kinect sensor: A review, IEEE Transactions on Cybernetics, vol.43, issue.5, pp.1318-1334

M. T. Harandi, C. Sanderson, A. Wiliem, and B. C. Lovell, Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures, 2012 IEEE Workshop on the Applications of Computer Vision (WACV), pp.433-439
DOI : 10.1109/WACV.2012.6163005

D. Huang, Y. Wang, S. Yao, and F. De-la-torre, Sequential maxmargin event detectors, European Conference on Computer Vision (ECCV), pp.17-125, 2014.
DOI : 10.1007/978-3-319-10578-9_27

URL : http://ca.cs.cmu.edu/papers/sequential.pdf

Z. Jiang, Z. Lin, and L. S. Davis, Learning a discriminative dictionary for sparse coding via label consistent K-SVD, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995354

S. H. Joshi, E. Klassen, A. Srivastava, and I. Jermyn, A Novel Representation for Riemannian Analysis of Elastic Curves in Rn, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-7, 2007.
DOI : 10.1109/CVPR.2007.383185

S. B. Kang and K. Ikeuchi, Temporal segmentation of tasks from human hand motion, 1993.

S. B. Kang and K. Ikeuchi, Determination of motion breakpoints in a task sequence from human hand motion, IEEE International Conference on Robotics and Automation, 1994.

S. Karaman, J. Benois-pineau, V. Dovgalecs, R. Mégret, J. Pinquier et al., Hierarchical Hidden Markov Model in detecting activities of daily living in wearable videos for studies of dementia, Multimedia Tools and Applications, vol.10, issue.6, pp.412237-2252, 2008.
DOI : 10.4017/gt.2010.09.02.285.00

URL : https://hal.archives-ouvertes.fr/hal-00639014

H. Karcher, Riemannian center of mass and mollifier smoothing, Communications on Pure and Applied Mathematics, vol.3, issue.5
DOI : 10.1007/BFb0079185

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 : 10.1145/1553374.1553523

URL : http://journals.sagepub.com/doi/pdf/10.1177/0278364913478446

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

B. Kwolek and M. Kepski, Human fall detection on embedded platform using depth maps and wireless accelerometer, Computer Methods and Programs in Biomedicine, vol.117, issue.3, pp.489-501
DOI : 10.1016/j.cmpb.2014.09.005

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

A. M. Lehrmann, P. V. Gehler, and S. Nowozin, Efficient Nonlinear Markov Models for Human Motion, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1314-1321, 2014.
DOI : 10.1109/CVPR.2014.171

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

I. Lillo, A. Soto, and J. C. Niebles, Discriminative Hierarchical Modeling of Spatio-temporally Composable Human Activities, 2014 IEEE Conference on Computer Vision and Pattern Recognition, p.31, 2014.
DOI : 10.1109/CVPR.2014.109

L. Liu and L. Shao, Learning discriminative representations from RGB-D video data, Proc. of the Twenty-Third Int. Joint Conf. on Artificial Intelligence, IJCAI'13, pp.1493-1500

L. Liu, L. Shao, X. Zhen, and X. Li, Learning Discriminative Key Poses for Action Recognition, IEEE Transactions on Cybernetics, vol.43, issue.6, pp.1860-1870, 2013.
DOI : 10.1109/TSMCB.2012.2231959

URL : http://lshao.staff.shef.ac.uk/pub/KeyPoses_TC2013.pdf

C. Lu, J. Jia, and C. Tang, Range-Sample Depth Feature for Action Recognition, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.104

Y. M. Lui, Tangent Bundles on Special Manifolds for Action Recognition, IEEE Transactions on Circuits and Systems for Video Technology, vol.22, issue.6, pp.930-942, 2012.
DOI : 10.1109/TCSVT.2011.2181452

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
DOI : 10.1109/ICCV.2013.227

M. Martinez and L. E. Sucar, Learning dynamic naive bayesian classifiers, Proceedings of the Twenty-First International FLAIRS Conference, 2008.

A. Marzal and E. Vidal, Computation of normalized edit distance and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.9, pp.926-932, 1993.
DOI : 10.1109/34.232078

G. Mastorakis and D. Makris, Fall detection system using Kinect???s infrared sensor, Journal of Real-Time Image Processing, vol.33, issue.11, pp.635-646
DOI : 10.1016/S0021-9290(00)00117-2

B. Ni, Y. Pei, P. Moulin, and S. Yan, Multi-level depth and image fusion for human activity detection, IEEE Transactions on Cybernetics, vol.43, issue.5, pp.1383-1394, 2013.

B. Ni, G. Wang, and P. Moulin, Rgbd-hudaact: A colordepth video database for human daily activity recognition, International Conference on Computer Vision Workshops, 2011.

F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, Sequence of the most informative joints (smij): A new representation for human skeletal action recognition, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2012.

F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition, Journal of Visual Communication and Image Representation, vol.25, issue.1, pp.24-38
DOI : 10.1016/j.jvcir.2013.04.007

E. Ohn-bar and M. M. Trivedi, Joint angles similarities and HOG 2 for action recognition, Proc. CVPR Work. on Human Activity Understanding from 3D Data, pp.465-470, 2013.
DOI : 10.1109/cvprw.2013.76

URL : http://cvrr.ucsd.edu/eshed/papers/OhnBarHAU3D13.pdf

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-lopez, A. A. Chaaraoui, and F. Florez-revuelta, A discussion on the validation tests employed to compare human action recognition methods using the msr action3D dataset, p.2015

A. Paiement, L. Tao, S. Hannuna, M. Camplani, D. Damen et al., Online quality assessment of human movement from skeleton data, Proceedings of British Machine Vision Conference (BMVC)

J. Pinquier, S. Karaman, L. Letoupin, P. Guyot, R. Mégret et al., Strategies for multiple feature fusion with hierarchical hmm: application to activity recognition from wearable audiovisual sensors, International Conference on Pattern Recognition (ICPR), 2012.
URL : https://hal.archives-ouvertes.fr/hal-00853854

R. Poppe, A survey on vision-based human action recognition, Image and Vision Computing, vol.28, issue.6, pp.976-990, 2010.
DOI : 10.1016/j.imavis.2009.11.014

L. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989.

M. A. Rahman, A. M. Qamar, M. A. Ahmed, M. A. Rahman, and S. Basalamah, Multimedia interactive therapy environment for children having physical disabilities, International conference on multimedia retrieval (ICMR), 2013.

H. Rahmani, A. Mahmood, D. Q. Huynh, and A. Mian, HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition, European Conference on Computer Vision (ECCV), p.18, 2014.
DOI : 10.1007/978-3-319-10605-2_48

Z. Ren, J. Yuan, and Z. Zhang, Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera, Proceedings of the 19th ACM international conference on Multimedia, MM '11, pp.1093-1096
DOI : 10.1145/2072298.2071946

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, pp.646-651, 2014.
DOI : 10.1109/WACV.2014.6836041

S. Saini, D. Rambli, S. Sulaiman, M. Zakaria, and S. Shukri, A lowcost game framework for a home-based stroke rehabilitation system, International Conference on Computer Information Science (ICCIS), 2012.

A. A. Salah, T. Gevers, N. Sebe, and A. Vinciarelli, Challenges of Human Behavior Understanding, International Workshop on Human Behavior Understanding, pp.1-12
DOI : 10.1109/34.824823

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, pp.479-485, 2013.
DOI : 10.1109/CVPRW.2013.77

L. Shao, X. Zhen, D. Tao, and X. Li, Spatio-Temporal Laplacian Pyramid Coding for Action Recognition, IEEE Transactions on Cybernetics, vol.44, issue.6, pp.817-827
DOI : 10.1109/TCYB.2013.2273174

S. Shirazi, M. T. Har, C. Sanderson, A. Alavi, and B. C. Lovell, Clustering on Grassmann manifolds via kernel embedding with application to action analysis, 2012 19th IEEE International Conference on Image Processing, pp.781-784
DOI : 10.1109/ICIP.2012.6466976

J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio et al., Real-time human pose recognition in parts from single depth images, Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp.1-8, 2011.

R. Slama, H. Wannous, M. Daoudi, and A. Srivastava, Accurate 3D action recognition using learning on the Grassmann manifold, Pattern Recognition, vol.48, issue.2, pp.556-567, 2015.
DOI : 10.1016/j.patcog.2014.08.011

URL : https://hal.archives-ouvertes.fr/hal-01056399

A. Srivastava, E. Klassen, S. H. Joshi, and I. Jermyn, Shape Analysis of Elastic Curves in Euclidean Spaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.7, pp.1415-1428, 2011.
DOI : 10.1109/TPAMI.2010.184

T. Starner, J. Weaver, and A. Pentland, Real-time American sign language recognition using desk and wearable computer based video, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.12, pp.1371-1375, 1998.
DOI : 10.1109/34.735811

URL : http://luthuli.cs.uiuc.edu/~daf/courses/Signals AI/Papers/HMMs/00735811.pdf

J. Sung, C. Ponce, B. Selman, and A. Saxena, Human activity detection from rgbd images, AAAI workshop on Plan, Activity and Intent Recognition (PAIR), 2011.

Y. Tian, L. Cao, Z. Liu, and Z. Zhang, Hierarchical Filtered Motion for Action Recognition in Crowded Videos, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.42, issue.3, pp.313-323, 2012.
DOI : 10.1109/TSMCC.2011.2149519

X. Tong, P. Xu, and X. Yan, Research on Skeleton Animation Motion Data Based on Kinect, 2012 Fifth International Symposium on Computational Intelligence and Design, p.2
DOI : 10.1109/ISCID.2012.238

P. Turaga, R. Chellappa, V. S. Subrahmanian, and O. Udrea, Machine Recognition of Human Activities: A Survey, IEEE Transactions on Circuits and Systems for Video Technology, pp.1473-1488, 2008.
DOI : 10.1109/TCSVT.2008.2005594

P. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa, Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.11, pp.2273-2286, 2011.
DOI : 10.1109/TPAMI.2011.52

A. Veeraraghavan, A. K. Roy-chowdhury, and R. Chellappa, Matching shape sequences in video with applications in human movement analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.12, pp.1896-1909, 2005.
DOI : 10.1109/TPAMI.2005.246

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, 2014.
DOI : 10.1109/CVPR.2014.82

A. W. Vieira, E. R. Nascimento, G. L. Oliveira, Z. Liu, and M. F. Campos, STOP: Space-Time Occupancy Patterns for 3D Action Recognition from Depth Map Sequences, Iberoamerican Congress on Pattern Recognition, pp.252-259, 2012.
DOI : 10.1007/978-3-642-33275-3_31

J. Wang, Z. Liu, J. Chorowski, Z. Chen, and Y. Wu, Robust 3D Action Recognition with Random Occupancy Patterns, Proc. Europ
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.1-8, 2012.
DOI : 10.1109/CVPR.2012.6247813

P. Wei, Y. Zhao, N. Zheng, and S. Zhu, Modeling 4D Human-Object Interactions for Event and Object Recognition, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.406

P. Wei, N. Zheng, Y. Zhao, and S. Zhu, Concurrent Action Detection with Structural Prediction, 2013 IEEE International Conference on Computer Vision, p.18, 2013.
DOI : 10.1109/ICCV.2013.389

URL : http://www.stat.ucla.edu/~sczhu/papers/Conf_2013/concurrent_action_iccv2013.pdf

D. Weinland, R. Ronfard, and E. Boyer, A survey of vision-based methods for action representation, segmentation and recognition, Computer Vision and Image Understanding, vol.115, issue.2
DOI : 10.1016/j.cviu.2010.10.002

URL : https://hal.archives-ouvertes.fr/inria-00459653

L. Xia and J. K. Aggarwal, Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.2834-2841, 2013.
DOI : 10.1109/CVPR.2013.365

L. Xia, C. Chen, and J. K. Aggarwal, View invariant human action recognition using histograms of 3D joints, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.20-27, 2012.
DOI : 10.1109/CVPRW.2012.6239233

L. Xia, I. Gori, J. K. Aggarwal, and M. S. Ryoo, Robot-centric Activity Recognition from First-Person RGB-D Videos, 2015 IEEE Winter Conference on Applications of Computer Vision, 2015.
DOI : 10.1109/WACV.2015.54

D. Xu and S. Chang, Visual Event Recognition in News Video using Kernel Methods with Multi-Level Temporal Alignment, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383226

X. Yang and Y. Tian, Eigenjoints-based action recognition using naive-bayes-nearest-neighbor, Proc. Work. on Human Activity Understanding from 3D Data, pp.14-19, 2012.
DOI : 10.1109/cvprw.2012.6239232

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

URL : http://yangxd.org/publications/papers/SNV.pdf

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

M. Ye, Q. Zhang, L. Wang, J. Zhu, R. Yang et al., A Survey on Human Motion Analysis from Depth Data, CVPR Tutorial on RGBD Cameras, p.17, 2013.
DOI : 10.1007/978-3-642-44964-2_8

G. Yu, Z. Liu, and J. Yuan, Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction, Asian Conference on Computer Vision (ACCV), pp.17-125, 2014.
DOI : 10.1007/978-3-319-16814-2_4

K. Yun, J. Honorio, D. Chattopadhyay, T. L. Berg, and D. Samaras, Two-person interaction detection using body-pose features and multiple instance learning, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012.
DOI : 10.1109/CVPRW.2012.6239234

URL : http://www.cs.sunysb.edu/%7Eial/content/papers/2012/kiwon_hau3d12.pdf

M. Zanfir, M. Leordeanu, and C. Sminchisescu, The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection, 2013 IEEE International Conference on Computer Vision, pp.2752-2759, 2013.
DOI : 10.1109/ICCV.2013.342

Q. Zhang and B. Li, Discriminative K-SVD for dictionary learning in face recognition, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539989

Y. Zhao, H. Cheng, and L. Yang, 3D sparse quantization for feature learning in action recognition, 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)
DOI : 10.1109/ChinaSIP.2015.7230404

F. Zhou, F. De-la-torre, and J. Hodgins, Hierarchical aligned cluster analysis for temporal clustering of human motion