O. Tuzel, F. Porikli, and P. Meer, Region Covariance: A Fast Descriptor for Detection and Classification, Proc. Eur. Conf. Comp. Vis, pp.589-600, 2006.
DOI : 10.1023/B:VISI.0000029664.99615.94

O. Tuzel, F. Porikli, and P. Meer, Human Detection via Classification on Riemannian Manifolds, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383197
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.641.7999

K. Mikolajczyk, C. Schmid, and A. Zisserman, Human Detection Based on a Probabilistic Assembly of Robust Part Detectors, Proc. Eur. Conf. Comp. Vis, pp.69-81, 2004.
DOI : 10.1007/978-3-540-24670-1_6
URL : https://hal.archives-ouvertes.fr/inria-00548537

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), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

B. Wu and R. Nevatia, Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors, Proc. IEEE Int. Conf. Comp. Vis, pp.90-97, 2005.

C. Wöhler and J. Anlauf, An adaptable time-delay neural-network algorithm for image sequence analysis, IEEE Transactions on Neural Networks, vol.10, issue.6, pp.1531-1536, 1999.
DOI : 10.1109/72.809100

C. Papageorgiou and T. Poggio, A trainable system for object detection, International Journal of Computer Vision, vol.38, issue.1, pp.15-33, 2000.
DOI : 10.1023/A:1008162616689

P. Viola, M. J. Jones, and D. Snow, Detecting pedestrians using patterns of motion and appearance, Proc. IEEE Int. Conf. Comp. Vis, 2003.

Y. Wu and T. Yu, A field model for human detection and tracking, IEEE Trans. Pattern Anal. Mach. Intell, vol.28, issue.5, pp.753-765, 2006.

D. M. Gavrila and S. Munder, Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle, International Journal of Computer Vision, vol.10, issue.6, pp.41-59, 2007.
DOI : 10.1007/s11263-006-9038-7

P. Viola and M. J. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2004.
DOI : 10.1023/B:VISI.0000013087.49260.fb
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.9805

Y. Amit, D. Geman, and X. Fan, A coarse-to-fine strategy for multiclass shape detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.12, pp.1606-1621, 2004.
DOI : 10.1109/TPAMI.2004.111

S. Munder and D. M. Gavrila, An Experimental Study on Pedestrian Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.11, pp.1863-1868, 2006.
DOI : 10.1109/TPAMI.2006.217

K. Levi and Y. Weiss, Learning object detection from a small number of examples: the importance of good features, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004.
DOI : 10.1109/CVPR.2004.1315144

S. Paisitkriangkrai, C. Shen, and J. Zhang, Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features, IEEE Transactions on Circuits and Systems for Video Technology, vol.18, issue.8, 2008.
DOI : 10.1109/TCSVT.2008.928213

G. Mori, X. Ren, A. Efros, and J. Malik, Recovering human body configurations: combining segmentation and recognition, Proc. IEEE Conf, pp.326-333, 2004.
DOI : 10.1109/cvpr.2004.1315182

P. Sabzmeydani and G. Mori, Detecting Pedestrians by Learning Shapelet Features, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383134
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.8978

Q. Zhu, S. Avidan, M. Yeh, and K. Cheng, Fast human detection using a cascade of histograms of oriented gradients, Proc. IEEE Conf. Comp. Vis. Patt. Recogn, pp.1491-1498, 2006.

I. Laptev, Improvements of Object Detection Using Boosted Histograms, Procedings of the British Machine Vision Conference 2006, pp.949-958, 2006.
DOI : 10.5244/C.20.97

R. Duda, P. Hart, and D. Stork, Pattern Classification, 2001.

T. Joachims, Making large-Scale SVM Learning Practical Advances in Kernel Methods -Support Vector Learning, 1999.

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236