*. Start-training-initializing-weights, Starting AdaBoost run

R. Performig and .. , End of ROC analysis. Begin of AdaBoost ROC analyze for validation set... Filling classification matrix.... Performing steps analysis

Y. Amit, D. Geman, and K. Wilder, Joint induction of shape features and tree classiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.22, p.13001305, 1997.

S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, p.174188, 2002.

N. Bergman, Recursive Bayesian estimation : Navigation and tracking applications, 1999.

M. Bertozzi, A. Broggi, A. Fascioli, and M. Sechi, Shaped-based Pedestrian detection, Proceedings of the IEEE Intelligent Vehicles Symposium, p.215, 2000.

M. Bertozzi, A. Broggi, A. Fascioli, and P. Lombardi, Vision-based pedestrian detection: will ants help?, Intelligent Vehicle Symposium, 2002. IEEE, 2002.
DOI : 10.1109/IVS.2002.1187919

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

]. R. Bish and . Bishop, Whatever Happened to Automated Highway Systems (AHS)

C. J. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Min. Knowl. Discov, vol.2, issue.2, p.121167, 1998.

]. B. Carlin, N. G. Polson, and D. S. Stoer, A Monte Carlo approach to nonnormal and nonlinear state-space modeling Improved particle lter for nonlinear problems, Proc. Inst. Elect. Eng., Radar, Sonar, Navig, pp.493500-493527, 1992.

]. D. Crisan, P. D. Moral, and T. J. Lyons, Non-linear ltering using branching and interacting particle systems, Markov Processes Related Fields, vol.5, issue.3, p.293319, 1999.

A. Doucet, S. Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian ltering, Statist. Comput, vol.10, issue.3, 2001.

H. Drucker, R. Schapire, and P. Simard, Boosting Performance in Neural Networks, International Journal of Pattern Recognition and Articial Intelligence, vol.7, issue.4, p.705719, 1993.

M. Pontil, C. Papagcorgiou, and T. Poggio, Image representations for object detection using kernel classiers, Asian Conference on Computer Vision, p.687692, 2000.

M. Fink and P. Perona, Mutual Boosting for Contextual Inference, Advances in Neural Information Processing Systems, 2004.

Y. Freund, Boosting a weak learning algorithm by majority, Proceedings of the Third Annual Workshop on Computational Learning Theory, p.202216, 1990.

R. E. Freund and . Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, European Conference on Computational Learning Theory, p.2337, 1995.
DOI : 10.1006/jcss.1997.1504

R. Freund and . Schapire, A short introduction to boosting, Journal of Japanese Society for Articial Intelligence, vol.14, issue.5, p.771780, 1999.

[. B. and G. Reichart, Prometheus : Vision desìntelligenten Automobils' aufìntelligenter Straÿe' ? Versuch einer kritischen Würdigung, ATZ Automobiltechnische Zeitschrift, vol.97, issue.4, 1995.

S. Godsill, A. Doucet, and M. West, Methodology for Monte Carlo smoothing with application to time-varying autoregressions, Proc. Int. Symp. Frontiers Time Series Modeling, p.7781, 2000.

N. Gordon, D. Salmond, and A. F. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, Proc. Inst. Elect. Eng, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

T. Hideo, Intelligent transportation systems in Japan, Public Roads, vol.60, issue.2, p.174188, 1996.

I. Haritaoglu, D. Harwood, and L. Davis, Who, when, where, what : a real time system for detecting and tracking people, Proceedings of the Third Face and Gesture Recognition Conference, p.222227, 1998.

L. Itti, C. Koch, and E. Niebur, A model of saliency-based visual attention forrapid scene analysis, 222 RÉFÉRENCES BIBLIOGRAPHIQUES [Jach 03] J. Jachalsky, M. Wahle, P. Pirsch, S. Capperon, W. Gehrke, W. Kruijtzer, and A. Nuñez. A Core for Ambient and Mobile Intelligent Imaging Applications, p.12541259, 1998.

A. H. Jazwinski, Stochastic Processes and Filtering Theory, 1970.

]. K. Kanazawa, D. Koller, and S. J. Russell, Stochastic simulation algorithms for dynamic probabilistic networks, Proc. Eleventh Annu. Conf. Uncertainty AI, p.346351, 1995.

M. Kearns and L. G. Valiant, Learning Boolean Formulae or Finite Automata is as Hard as Factoring, 1988.

M. Kearns and L. G. Valiant, Cryptographic Limitations on Learning Boolean Formulae and Finite Automata, Proceedings of the Twenty First Annual ACM Symposium on Theory of Computing, p.433444, 1989.

M. J. Kearns and U. V. Vazirani, An Introduction to Computational Learning Theory, 1994.

M. Leung and Y. H. Yang, Human body motion segmentation in a complex scene, Pattern Recognition, vol.20, issue.1, p.5564, 1987.
DOI : 10.1016/0031-3203(87)90017-3

A. Levin, P. Viola, and Y. Freund, Unsupervised Improvement of Visual Detectors using Co-Training, Proceedings of the International Conference on Computer Vision, p.626633, 2003.

]. J. Liu and R. Chen, Sequential Monte Carlo methods for dynamical systems

J. Maccormick and A. Blake, A probabilistic exclusion principle for tracking multiple ob jects [Mall 89] S. Mallat. A theory for multiresolution signal decomposition : the wavelet representation, Proc. Int. Conf. Comput. Vision, pp.572578-67493, 1989.

A. Mohan, C. Papageorgiou, and T. Poggio, Example-Based Ob ject Detection in Images by Components, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.4, p.349361, 2001.

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, 1997.
DOI : 10.1109/CVPR.1997.609319

E. Osuna, R. Freund, and F. Girosi, Training support vector machines: an application to face detection, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997.
DOI : 10.1109/CVPR.1997.609310

]. C. Papa-98a, T. Papageorgiou, T. Evgeniou, and . Poggio, A trainable pedestrian detection system, Procs. IEEE Intelligent Vehicles Symposium, p.241246, 1998.

]. C. Papa-98b, T. Papageorgiou, M. Poggio, and . Oren, A general framework for object de- tection

C. Papageorgiou and T. Poggio, A Pattern Classication Approach to Dynamical Object Detection, p.12231228, 1999.

B. Ripley, Stochastic Simulation, 1987.
DOI : 10.1002/9780470316726

R. E. Schapire, The Strength of Weak Learnability, 30th Annual Symposium on Foundations of Computer Science, p.2833, 1989.

R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, Boosting the margin: a new explanation for the effectiveness of voting methods, Machine Learning : Proceedings of the Fourteenth International Conference, p.322330, 1997.
DOI : 10.1214/aos/1024691352

R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, Boosting the margin: a new explanation for the effectiveness of voting methods, The Annals of Statistics, vol.26, issue.5, p.16511686, 1998.
DOI : 10.1214/aos/1024691352

B. Steux, P. Coulombeau, and C. Laurgeau, Maps : a framework for prototyping automotive multi-sensor applications, IEEE Intelligent Vehicles Symposium, 2000.

]. K. Tieu and P. Viola, Boosting Image Database Retrieval, 1999.

J. Tsotsos, S. Culhane, W. Wai, Y. Lai, N. Davis et al., Modeling visualattention via selective tuning, Articial Intelligence Journal, vol.78, issue.12, p.507545, 1995.

]. T. Tsukiyama and Y. Shirai, Detection of the movements of persons from a sparse sequence of TV images, Pattern Recognition, vol.18, issue.3, p.207213, 1985.

L. G. Valiant, A Theory of the Learnable, Communications of the ACM, vol.27, issue.11, p.11341142, 1984.

V. N. Vapnik, The nature of Statistical Learning Theory, 1995.

M. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, pp.511-518, 2001.

P. Viola, M. J. Jones, and D. Snow, Robust Real-time Object Detection Detecting Pedestrians using Patterns of Motion and Appearance, IEEE International Conference on Computer Vision, p.734741, 2002.

R. Wittebrood and G. De-haan, Real-time recursive motion segmentation of video data on a programmable device, IEEE Transactions on Consumer Electronics, p.559567, 2001.
DOI : 10.1109/30.964146