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), 2005.
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

P. Dollár, C. Wojek, B. Schiele, and P. Perona, Pedestrian detection : An evaluation of the state of the art. Pattern Analysis and Machine Intelligence, 2011.

M. Enzweiler and D. M. Gavrila, Monocular pedestrian detection : Survey and experiments. Pattern Analysis and Machine Intelligence, 2009.
DOI : 10.1109/tpami.2008.260

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5794

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, 2009.
DOI : 10.1007/s11263-009-0275-4

Y. Freund and R. Schapire, A short introduction to boosting, J. Japan. Soc. for Artif. Intel, 1999.

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression : a statistical view of boosting, Annals of Statistics, 1998.

D. Gerónimo, A. M. López, A. D. Sappa, and T. Graf, Survey of pedestrian detection for advanced driver assistance systems. Pattern Analysis and Machine Intelligence, 2010.

H. Grabner and H. Bischof, On-line Boosting and Vision, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.215

C. Leistner, A. Saffari, P. M. Roth, and H. Bischof, On robustness of on-line boosting - a competitive study, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009.
DOI : 10.1109/ICCVW.2009.5457451

A. Levin, P. Viola, and Y. Freund, Unsupervised improvement of visual detectors using co-training, Int. Conf. on Computer Vision, 2003.

C. Rosenberg, M. Hebert, and H. Schneiderman, Semi-Supervised Self-Training of Object Detection Models, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05), Volume 1, 2005.
DOI : 10.1109/ACVMOT.2005.107

R. E. Schapire and Y. Singer, Improved boosting algorithms using confidence-rated predictions, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, 1999.
DOI : 10.1145/279943.279960

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.2440

S. Stalder, H. Grabner, and L. Van-gool, Exploring context to learn scene specific object detectors, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2009.

C. Stauffer and W. E. Grimson, Adaptive background mixture models for real-time tracking, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999.
DOI : 10.1109/CVPR.1999.784637

V. N. Vapnik, The nature of statistical learning theory, 1995.

P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.990517

B. Wu, Part based object detection, segmentation, and tracking by boosting simple feature based weak classifiers, 2008.