H. Bilen, V. Namboodiri, V. Gool, and L. , Object and Action Classification with Latent Variables, Procedings of the British Machine Vision Conference 2011, 2011.
DOI : 10.5244/C.25.17

G. Bradski and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library Learning spatiotemporal graphs of human activities, 2008.

T. Brox and J. Malik, Object Segmentation by Long Term Analysis of Point Trajectories, 2010.
DOI : 10.1007/978-3-642-15555-0_21

N. Dalal, B. Triggs, and C. Schmid, Human Detection Using Oriented Histograms of Flow and Appearance, 2006.
DOI : 10.1023/A:1008162616689

URL : https://hal.archives-ouvertes.fr/inria-00548587

D. Castro, E. Morandi, C. Duda, R. Hart, P. Stork et al., Registration of translated and rotated images using finite Fourier transforms Graph theory Pattern classification Dupé F, Brun L (2008) Hierarchical bag of paths for kernel based shape classification. SSSPR Farnebäck G (2003) Two-frame motion estimation based on polynomial expansion Object detection with discriminatively trained part based models Stable and efficient gaussian process calculations Spectral grouping using the Nystrom method, PAMI Diestel R Image Analysis Felzenszwalb PF, 1987.

M. Fradet, P. Robert, and P. Pérez, Clustering point trajectories with various life-spans Actom Sequence Models for Efficient Action Detection Recognizing activities with cluster-trees of tracklets Action recognition using mined hierarchical compound features shape context and distance transform for action recognition, PAMI Grundmann M, p.3, 2008.

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning, 2008.

S. Hongeng and R. Nevatia, Large-scale event detection using semihidden markov models In: ICCV Ikizler-Cinbis N, Sclaroff S (2010) Object, scene and actions: Combining multiple features for human action recognition, 2003.

Y. Jiang, Q. Dai, X. Xue, W. Liu, C. Ngo et al., Trajectory-Based Modeling of Human Actions with Motion Reference Points Recognizing Human Actions by Learning and Matching Shape-Motion Prototype Trees Motion Interchange Patterns for Action Recognition in Unconstrained Videos Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, 2010.

H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, T. Serre et al., HMDB: a large video database for human motion recognition On space-time interest points Learning realistic human actions from movies, In: ICCV Laptev I IJCV Laptev I, 2005.

B. Laxton, J. Lim, and D. Kriegman, Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383074

J. Lezama, K. Alahari, J. Sivic, and I. Laptev, Track to the future: Spatio-temporal video segmentation with long-range motion cues, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.6044588

URL : https://hal.archives-ouvertes.fr/hal-00817961

J. Liu, B. Kuipers, and S. Savarese, Recognizing human actions by attributes, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995353

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.463.8447

D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

S. Maji, A. Berg, and J. Malik, Classification using intersection kernel support vector machines is efficient, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587630

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.3974

M. Marszalek, I. Laptev, C. Schmid, J. Cvpr-niebles, L. Fei-fei et al., Actions in context Representing pairwise spatial and temporal relations for action recognition Action recognition with motionappearance vocabulary forest Hierarchical model of shape and appearance for human action classification, L (2010) Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification, 2007.

N. Oliver, B. Rosario, A. Pentland, . Modeling-human-interactions, A. Pablo et al., Contour detection and hierarchical image segmentation High Five: Recognising Human Interactions in TV Shows Explicit modeling of human-object interactions in realistic videos. PAMI Raptis M, Kokkinos I, Soatto S (2012) Discovering Discriminative Action Parts from Mid-Level Video Representations Incremental action recognition using feature-tree Action Bank: A High-Level Representation of Activity in Video Learning discriminative spacetime actions from weakly labelled videos Nonlinear component analysis as a kernel eigenvalue problem Web-scale k-means clustering Kernel methods for pattern analysis, BMVC Schölkopf B, Smola AJ Learning with Kernels Schölkopf B Neural computation Sculley D, 1998.

J. Shi and J. Malik, Motion segmentation and tracking using normalized cuts, 1998.

J. Shi, M. J. Shi, J. Tomasi, and C. , Normalized cuts and image segmentation Good features to track Kernel on bag of paths for measuring similarity of shapes, European Symposium on Artificial Neural Networks, pp.1-6, 1994.

R. Szeliski, Computer vision: algorithms and applications, 2010.
DOI : 10.1007/978-1-84882-935-0

K. Tang, L. Fei-fei, and D. Koller, Learning latent temporal structure for complex event detection, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6247808

S. Todorovic, Human Activities as Stochastic Kronecker Graphs, 2012.
DOI : 10.1007/978-3-642-33709-3_10

E. Vig, M. Dorr, and D. Cox, Space-variant descriptor sampling for action recognition based on saliency and eye movements Dense trajectories and motion boundary descriptors for action recognition, 2012.

Y. Wang and G. Mori, Hidden part models for human action recognition: Probabilistic vs. max-margin Using the Nyström method to speed up kernel machines, PAMI Williams C, Seeger M, 2001.

G. Yu, J. Yuan, and Z. Liu, Propagative Hough Voting for Human Activity Recognition, 2012.