Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, 95 European Conference on Computational Learning Theory, pp.23-37, 1995.
DOI : 10.1006/jcss.1997.1504

Y. Freund and R. E. Schapire, A short introduction to boosting, Journal of Japanese Society for Artificial Intelligence, vol.14, issue.5, pp.771-780, 1999.

R. Meir, G. Ratsch, S. Mendelson, and A. Smola, An Introduction to Boosting and Leveraging, Advanced Lectures on Machine Learning, pp.119-184, 2003.
DOI : 10.1007/3-540-36434-X_4

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, p.511, 2001.
DOI : 10.1109/CVPR.2001.990517

P. Viola, M. J. Jones, and D. Snow, « Detecting pedestrians using patterns of motion and appearance, IEEE International Conference on Computer Vision (ICCV'2003), pp.734-741, 2003.

Y. Abramson and B. Steux, Illumination-independent pedestrian detection in real-time, 2003.

Y. Abramson, B. Steux, and H. Ghorayeb, Yet Even Faster (YEF) real-time object detection, Proceedings of International Workshop on Automatic Learning and Real-Time (ALART'05), p.5, 2005.
DOI : 10.1504/IJISTA.2007.012476

Y. Abramson, F. Moutarde, B. Steux, and B. Stanciulescu, COMBINING ADABOOST WITH A HILL-CLIMBING EVOLUTIONARY FEATURE SEARCH FOR EFFICIENT TRAINING OF PERFORMANT VISUAL OBJECT DETECTORS, Applied Artificial Intelligence, pp.737-744, 2006.
DOI : 10.1142/9789812774118_0104

B. Stanciulescu, A. Breheret, and F. Moutarde, Introducing New AdaBoost Features for Real-Time Vehicle Detection, Proceedings of COGIS2007 Cognitive Systems with Interactive Sensors, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00422587

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

S. Agarwal, A. Awan, and D. Roth, Learning to detect objects in images via a sparse, part-based representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1475-1490, 2004.
DOI : 10.1109/TPAMI.2004.108

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.20-26, 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

Q. Zhu, M. Yeh, K. Cheng, and S. Avidan, Fast Human Detection Using a Cascade of Histograms of Oriented Gradients, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.17-22, 2006.

S. Baluja, M. Sahami, and H. A. Rowley, Efficient face orientation discrimination, 2004 International Conference on Image Processing, 2004. ICIP '04., pp.589-592, 2004.
DOI : 10.1109/ICIP.2004.1418823

L. Leyrit, T. Chateau, C. Tournayre, and J. Lapresté, Association of AdaBoost and Kernel based machine learning methods for visual pedestrian recognition, 2008 IEEE Intelligent Vehicles Symposium, 2008.
DOI : 10.1109/IVS.2008.4621294
URL : https://hal.archives-ouvertes.fr/hal-01295884

N. Pettersson, L. Petersson, and L. Andersson, The histogram feature - a resource-efficient Weak Classifier, 2008 IEEE Intelligent Vehicles Symposium, 2008.
DOI : 10.1109/IVS.2008.4621174

A. Treptow and A. Zell, Combining Adaboost learning and evolutionary search to select features for real-time object detection, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), pp.2107-2113, 2004.
DOI : 10.1109/CEC.2004.1331156