M. S. Arulampalam, S. Maskell, and N. Gordon, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002.
DOI : 10.1109/78.978374

A. Blake, M. Isard, and D. Reynard, Learning to track the visual motion of contours, Artificial Intelligence, vol.78, issue.1-2, pp.101-134, 1995.
DOI : 10.1016/0004-3702(95)00032-1

A. Buchanan and A. W. Fitzgibbon, Combining local and global motion models for feature point tracking, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383236

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.27-28, 2011.
DOI : 10.1145/1961189.1961199

A. Cuzol and É. Mémin, A stochastic filter for fluid motion tracking, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.396-402, 2005.
DOI : 10.1109/ICCV.2005.21
URL : https://hal.archives-ouvertes.fr/hal-00386461

P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Machine Learning, pp.3-42, 2006.
DOI : 10.1007/s10994-006-6226-1
URL : https://hal.archives-ouvertes.fr/hal-00341932

H. Grabner, C. Leistner, and H. Bischof, Semi-supervised On-Line Boosting for Robust Tracking, ECCV, pp.234-247, 2008.
DOI : 10.1007/978-3-540-88682-2_19
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.5747

M. Isard and A. Blake, CONDENSATION -conditional density propagation for visual tracking, International Journal of Computer Vision, vol.29, issue.1, pp.5-28, 1998.
DOI : 10.1023/A:1008078328650

A. D. Jepson, D. J. Fleet, and T. F. El-maraghi, Robust online appearance models for visual tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.10, pp.1296-1311, 2003.
DOI : 10.1109/TPAMI.2003.1233903
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.4265

Z. Kalal, J. Matas, and K. Mikolajczyk, Tracking-Learning-Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.7, 2011.
DOI : 10.1109/TPAMI.2011.239

K. M. Kitani, T. Okabe, Y. Sato, and A. Sugimoto, Fast unsupervised ego-action learning for first-person sports videos, CVPR 2011, pp.3241-3248, 2011.
DOI : 10.1109/CVPR.2011.5995406
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.206.3332

A. Kowdle and T. Chen, Learning to Segment a Video to Clips Based on Scene and Camera Motion, ECCV, 2012.
DOI : 10.1007/978-3-642-33712-3_20

M. Kristan, J. Per?, S. Kova?i?, and A. Leonardis, A local-motion-based probabilistic model for visual tracking, Pattern Recognition, vol.42, issue.9, pp.2160-2168, 2009.
DOI : 10.1016/j.patcog.2009.01.002
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.2225

J. Kwon and K. Lee, Tracking by sampling trackers, ICCV, pp.1195-1202, 2011.

J. Liu, J. Luo, and M. Shah, Recognizing realistic actions from videos " in the wild, CVPR, 1996.

M. Lourenco and J. P. Barreto, Tracking Feature Points in Uncalibrated Images with Radial Distortion, ECCV, 2012.
DOI : 10.1007/978-3-642-33765-9_1

G. J. Oisin-mac-aodha, M. Brostow, and . Pollefeys, Segmenting video into classes of algorithm-suitability, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540099

F. Moosmann, E. Nowak, and F. Jurie, Randomized Clustering Forests for Image Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.9, pp.1632-1646, 2008.
DOI : 10.1109/TPAMI.2007.70822
URL : https://hal.archives-ouvertes.fr/inria-00548666

B. North, A. Blake, M. Isard, and J. Rittscher, Learning and classification of complex dynamics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.9, 2000.
DOI : 10.1109/34.877523

P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, Color-Based Probabilistic Tracking, ECCV, pp.661-675, 2002.
DOI : 10.1007/3-540-47969-4_44

A. Victor, I. Prisacariu, and . Reid, Nonlinear shape manifolds as shape priors in level set segmentation and tracking, CVPR, pp.2185-2192, 2011.

M. Rodriguez, S. Ali, and T. Kanade, Tracking in unstructured crowded scenes, 2009 IEEE 12th International Conference on Computer Vision, pp.1389-1396, 2009.
DOI : 10.1109/ICCV.2009.5459301
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.547

M. Rodriguez, J. Sivic, I. Laptev, and J. Audibert, Data-driven crowd analysis in videos, 2011 International Conference on Computer Vision, pp.1235-1242, 2011.
DOI : 10.1109/ICCV.2011.6126374
URL : https://hal.archives-ouvertes.fr/hal-00654256

E. Rosten and T. Drummond, Fusing points and lines for high performance tracking, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1508-1511, 2005.
DOI : 10.1109/ICCV.2005.104
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.4715

E. Rosten and T. Drummond, Machine Learning for High-Speed Corner Detection, ECCV, pp.430-443, 2006.
DOI : 10.1007/11744023_34
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.3991

B. Stenger, T. Woodley, and R. Cipolla, Learning to track with multiple observers, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.2647-2654, 2009.
DOI : 10.1109/CVPR.2009.5206634

M. Luka?ehovinluka?luka?ehovin, A. Kristan, and . Leonardis, An adaptive coupled-layer visual model for robust visual tracking, ICCV, pp.1363-1370, 2011.

C. Vondrick and D. Ramanan, Video Annotation and Tracking with Active Learning, Neural Information Processing Systems (NIPS), 2011.

H. Wang, A. Kläser, C. Schmid, and L. Cheng-lin, Action recognition by dense trajectories, CVPR 2011, pp.3169-3176, 2011.
DOI : 10.1109/CVPR.2011.5995407
URL : https://hal.archives-ouvertes.fr/inria-00583818

O. Williams, A. Blake, and R. Cipolla, Sparse Bayesian learning for efficient visual tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, 2005.
DOI : 10.1109/TPAMI.2005.167
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.466.2793

T. Woodley, B. Stenger, and R. Cipolla, Tracking Using Online Feature Selection and a Local Generative Model, Procedings of the British Machine Vision Conference 2007, 2007.
DOI : 10.5244/C.21.86
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.116.1691

R. Yao, Q. Shi, and C. Shen, Yanning Zhang, and Anton van den Hengel. Robust tracking with weighted online structured learning, ECCV, 2012.