I. P. Alonso, D. F. Llorca, M. A. Sotelo, L. M. Bergasa, P. R. De-toro et al., Combination of Feature Extraction Methods for SVM Pedestrian Detection, IEEE Transactions on Intelligent Transportation Systems, vol.8, issue.2, pp.292-307, 2007.
DOI : 10.1109/TITS.2007.894194

D. 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

Y. Freund and R. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, European Conference on Computational Learning Theory, pp.23-37, 1995.
DOI : 10.1006/jcss.1997.1504

T. Joachims, Making Large-Scale SVM Learning Practical Advances in Kernel Methods - Support Vector Learning, Burges and A. Smola, 1999.

S. Kang, H. Byun, and S. W. Lee, Real-Time Pedestrian Detection using Support Vector Machines, International Journal of Pattern Recognition and Artificial Intelligence, pp.17-405, 2002.
DOI : 10.1007/3-540-45665-1_21

K. Mikolajczyk, C. Schmid, and A. Zisserman, Human Detection Based on a Probabilistic Assembly of Robust Part Detectors, European Conference on Computer Vision, pp.69-82, 2004.
DOI : 10.1007/978-3-540-24670-1_6
URL : https://hal.archives-ouvertes.fr/inria-00548537

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

S. Munder, C. Schnorr, and D. M. Gavrila, Pedestrian Detection and Tracking Using a Mixture of View-Based Shape–Texture Models, IEEE Transactions on Intelligent Transportation Systems, vol.9, issue.2, pp.9-303, 2008.
DOI : 10.1109/TITS.2008.922943

M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio, Pedestrian detection using wavelet templates, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.193-199, 1997.
DOI : 10.1109/CVPR.1997.609319
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.182

Y. Ran, Q. Zheng, I. Weiss, L. S. Davis, W. Abd-almageed et al., Pedestrian Classification from Moving Platforms Using Cyclic Motion Pattern, International Conference on Image Processing, pp.854-857, 2005.

V. Sharma, J. W. Davis, Y. Ran, Q. Zheng, I. Weiss et al., Integrating Appearance and Motion Cues for Simultaneous Detection and Segmentation of Pedestrians, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4409035

D. Toth and T. Aach, Detection and recognition of moving objects using statistical motion detection and Fourier descriptors, 12th International Conference on Image Analysis and Processing, 2003.Proceedings., pp.430-435, 2003.
DOI : 10.1109/ICIAP.2003.1234088

O. Tuzel, F. Porikli, and P. Meer, Human Detection via Classification on Riemannian Manifolds, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.0-1, 2007.
DOI : 10.1109/CVPR.2007.383197

V. Vapnik, The Nature of Statistical Learning Theory, 1995.

B. Wu and R. Nevtia, Simultaneous Object Detection and Segmentation by Boosting Local Shape Feature based Classifier, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.0-1, 2007.
DOI : 10.1109/CVPR.2007.383042
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.9795

Q. Zhu, M. C. Yeh, K. T. Cheng, and S. Avidan, Fast Human Detection Using a Cascade of Histograms of Oriented Gradients, Computer Vision and Pattern Recognition, vol.2, p.1491, 2006.