G. Gâté and F. Nashashibi, An Approach for Robust Mapping, Detection, Tracking and Classification in Dynamic Environments, Proceedings of the IEEE International Conference on Advanced Robotics, 2009.

G. Gâté, A. Breheret, and F. Nashashibi, Centralised Fusion for Fast People Detection in Dense Environments, Proceedings of the IEEE International Conference on Robotics Automation (ICRA), 2009.

G. Gâté and F. Nashashibi, Fast Algorithm for People and Groups of People Detection and Tracking using a Laser Scanner, Proceedings of the IEEE Intelligent Vehicles Symposium (IV), 2009.

G. Gâté, A. Breheret, and F. Nashashibi, Fast Pedestrian Detection in Dense Environment with a Laser Scanner and a Camera, VTC Spring 2009, IEEE 69th Vehicular Technology Conference, 2009.
DOI : 10.1109/VETECS.2009.5073555

G. Gâté and F. Nashashibi, Using targets appearance to improve pedestrian classification with a laser scanner, 2008 IEEE Intelligent Vehicles Symposium, 2008.
DOI : 10.1109/IVS.2008.4621253

Y. Abramson, . Steux, . Bruno, . Ghorayeb, and . Hicham, Yet Even Faster (YEF) real-time object detection, International Journal of Intelligent Systems Technologies and Applications, vol.2, issue.2/3, pp.102-112, 2007.
DOI : 10.1504/IJISTA.2007.012476

C. Andrieu, . Davy, . Manuel, and A. Doucet, Efficient particle filtering for jump markov systems. Application to time-varying autoregressions, IEEE Transactions on Signal Processing, vol.51, issue.7, p.31, 2003.
DOI : 10.1109/TSP.2003.810284

K. O. Arras, O. M. Mozos, and W. Burgard, Using Boosted Features for the Detection of People in 2D Range Data. Pages 3402?3407 of: Robotics and Automation, IEEE International Conference on, p.47, 2007.

. Arulampalam, . Sanjeev, . Maskell, . Simon, . Gordon et al., A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking, IEEE Transactions on Signal Processing, vol.50, pp.174188-174218, 2001.

T. Bailey, Mobile Robot Localisation and Mapping in Extensive Outdoor Environments, p.27, 2002.

R. Benenson, Perception for driverless vehicle: design and implementation, p.29, 2008.

M. Bertozzi, A. Broggi, F. Chausse, F. Chausse, A. Fascioli et al., Shape-based pedestrian detection and localization, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems, p.47, 2003.
DOI : 10.1109/ITSC.2003.1251972
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.5491

P. Biber, The normal distributions transform: a new approach to laser scan matching, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), p.27, 2003.
DOI : 10.1109/IROS.2003.1249285

S. S. Blackman, Multiple hypothesis tracking for multiple target tracking. Aerospace and Electronic Systems Magazine, IEEE, vol.19, issue.518, p.32, 2004.

S. Blackman and R. Popoli, Design and Analysis of Modern Tracking Systems, p.32, 1999.

. Blom, A. Henk, and Y. Bar-shalom, The interacting multiple model algorithm for systems with Markovian switching coefficients, IEEE Transactions on Automatic Control, vol.33, issue.8, p.31, 1988.
DOI : 10.1109/9.1299

R. Brown, . Grover, and P. Y. Hwang, Introduction to Random Signals and Applied Kalman Filtering, p.23, 1997.

Y. Chen and G. Medioni, Object modeling by registration of multiple range images, Proceedings. 1991 IEEE International Conference on Robotics and Automation, p.26, 1991.
DOI : 10.1109/ROBOT.1991.132043

J. Cui, . Zha, . Hongbin, . Zhao, . Huijing et al., Laser-based detection and tracking of multiple people in crowds, Computer Vision and Image Understanding, vol.106, issue.2-3, pp.300-312, 2007.
DOI : 10.1016/j.cviu.2006.07.015

A. Dempster, A Generalization of Bayesian Inference. Pages 73?104 of: Classic Works of the Dempster-Shafer Theory of Belief Functions, p.73, 2008.

D. Dubois, La th??orie des possibilit??s, Revue de l'Electricit?? et de l'Electronique, vol.-, issue.07, p.73, 1985.
DOI : 10.3845/ree.2006.059

A. Elfes, Using occupancy grids for mobile robot perception and navigation, Computer, vol.22, issue.6, pp.46-57, 1989.
DOI : 10.1109/2.30720

A. Elfes, Occupancy grids: a probabilistic framework for robot perception and navigation, p.140, 1989.

M. Enzweiler and D. M. Gavrila, Monocular Pedestrian Detection: Survey and Experiments . Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.12, pp.31-2179, 2009.
DOI : 10.1109/tpami.2008.260
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5794

F. Fayad and V. Cherfaoui, Tracking objects using a laser scanner in driving situation based on modeling target shape. Pages 44?49 of: Proceedings of the IEEE Intelligent Vehicles Symposium, p.79, 2007.

F. Fayad, V. Cherfaoui, and G. Dherbomez, Updating confidence indicators in a multisensor pedestrian tracking system. Pages 156?161 of: Intelligent Vehicles Symposium, p.34, 2008.

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

Y. Freund and R. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, European Conference on Computational Learning Theory, p.70, 1995.
DOI : 10.1006/jcss.1997.1504

K. Fuerstenberg, . Linzmeier, T. Dirk, and K. C. Dietmayer, Pedestrian recognition and tracking of vehicles using a vehicle based multilayer laserscanner, Proceedings of the 10th World Congress on Intelligent Transport Systems, p.46, 2003.

K. Fuerstenberg, . Ch, K. C. Dietmayer, and V. Willhoeft, Pedestrian recognition in urban traffic using a vehicle based multilayer laserscanner, Intelligent Vehicle Symposium, 2002. IEEE, p.46, 2002.
DOI : 10.1109/IVS.2002.1187923

G. Gate and F. Nashashibi, Fast algorithm for pedestrian and group of pedestrians detection using a laser scanner, 2009 IEEE Intelligent Vehicles Symposium, p.33, 2009.
DOI : 10.1109/IVS.2009.5164476

G. Gate, A. Breheret, and F. Nashashibi, Centralized fusion for fast people detection in dense environment, 2009 IEEE International Conference on Robotics and Automation, p.34, 2009.
DOI : 10.1109/ROBOT.2009.5152645

S. Gidel, P. Checchin, C. Blanc, T. Chateau, and L. Trassoudaine, Parzen method for fusion of laserscanner data: Application to pedestrian detection, 2008 IEEE Intelligent Vehicles Symposium, p.33, 2008.
DOI : 10.1109/IVS.2008.4621166
URL : https://hal.archives-ouvertes.fr/hal-00344114

J. Gutmann and C. Schlegel, AMOS: comparison of scan matching approaches for self-localization in indoor environments. Advanced Mobile Robots, pp.61-87, 1996.

D. Hahnel, D. Schulz, and W. Burgard, Mobile robot mapping in populated environments, Advanced Robotics, vol.17, issue.7, p.38, 2003.
DOI : 10.1163/156855303769156965

S. Herman and . Michael, A Particle Filtering Approach To Joint Passive Radar Tracking And Target Classification, p.30, 2002.

R. Kalman and . Emil, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, p.30, 1960.
DOI : 10.1115/1.3662552

G. K. Kraetzschmar, G. Gassull, . Pags, . Uhl, . Klaus et al., Probabilistic Quadtrees for Variable-Resolution Mapping of Large Environments, Proceedings of the 5th IFAC, p.157, 2004.

J. Leal, Stochastic Environment Representation, p.121, 2003.

A. Mendes, L. C. Bento, and U. Nunes, Multi-target detection and tracking with a laser scanner. Pages 796?801 of: Intelligent Vehicles Symposium, IEEE, vol.46, p.53, 2004.

M. Montemerlo and S. Thrun, A multi-resolution pyramid for outdoor robot terrain perception, Proceedings of the AAAI National Conference on Artificial Intelligence, p.104, 2004.

L. Montesano, J. Minguez, and L. Montano, Modeling the Static and the Dynamic Parts of the Environment to Improve Sensor-based Navigation, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, p.38, 2005.
DOI : 10.1109/ROBOT.2005.1570822

. Oh, . Songhwai, . Russell, . Stuart, . Sastry et al., Markov Chain Monte Carlo Data Association for General Multiple-Target Tracking Problems, Proceedings of the IEEE Conference on Decision and Control, p.32, 2004.

A. Papoulis, S. Pillai, and . Unnikrishna, Probability, Random Variables and Stochastic Processes with Errata Sheet, p.24, 2002.

A. Petrovskaya and S. Thrun, Model Based Vehicle Tracking for Autonomous Driving in Urban Environments, Robotics: Science and Systems IV, p.91, 2008.
DOI : 10.15607/RSS.2008.IV.023

A. Petrovskaya and S. Thrun, Model based vehicle detection and tracking for autonomous urban driving, Autonomous Robots, vol.58, issue.2, pp.123139-123173, 2009.
DOI : 10.1007/s10514-009-9115-1

E. Prassler, J. Scholz, and P. Fiorini, Navigating a Robotic Wheelchair in a Railway Station during Rush Hour, The International Journal of Robotics Research, vol.18, issue.7, pp.711-727, 1999.
DOI : 10.1177/02783649922066529

C. Premebida and U. Nunes, A Multi-Target Tracking and GMM-Classifier for Intelligent Vehicles. Pages 313?318 of: Intelligent Transportation Systems Conference, p.46, 2006.

D. Schulz, . Burgard, . Wolfram, . Fox, . Dieter et al., Tracking multiple moving targets with a mobile robot using particle filters and statistical data association, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), p.33, 2001.
DOI : 10.1109/ROBOT.2001.932850

G. Shafer, Mathematical Theory of Evidence, p.73, 1976.

. Shao, . Zhao, . Huijing, K. Nakamura, K. Katabira et al., Detection and tracking of multiple pedestrians by using laser range scanners. Pages 2174?2179 of: Intelligent Robots and Systems, IROS 2007. IEEE/RSJ International Conference on, p.46, 2007.

X. Song and R. Nevatia, A Model-Based Vehicle Segmentation Method for Tracking. Pages 1124?1131 of: Computer Vision, IEEE International Conference on, p.32, 2005.

L. Spinello, R. Triebel, and R. Siegwart, Multimodal detection and tracking of pedestrians in urban environments with explicit ground plane extraction. Pages 1823?1829 of: Intelligent Robots and Systems, p.47, 2008.

B. Stanciulescu, A. Breheret, and F. Moutarde, Introducing New AdaBoost Features for Real-Time Vehicle Detection, Proceedings of Cognitive Systems with Interactive Sensors, p.71, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00422587

D. Streller, K. Furstenberg, and K. Dietmayer, Vehicle and object models for robust tracking in traffic scenes using laser range images, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems, p.46, 2002.
DOI : 10.1109/ITSC.2002.1041199

S. Thrun, Robotic mapping: A survey. Exploring Artificial Intelligence in the New Millenium, p.35, 2002.

J. K. Tugnait, Detection and estimation for abruptly changing systems. Pages 1357?1362 of, Proceedings of the 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, p.31, 1981.

P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features. Pages I of: Computer Vision and Pattern Recognition, Proceedings of the 2001 IEEE Computer Society Conference on, pp.511-518, 2001.

P. Viola, M. J. Jones, and D. Snow, Detecting pedestrians using patterns of motion and appearance. Pages, Proceedings. Ninth IEEE International Conference on, pp.734-741, 2003.

. Vu, . Trung-dung, and O. Aycard, Laser-based Detection and Tracking Moving Objects using Data-Driven Markov Chain Monte Carlo, IEEE International Conference on Robotics & Automation, pp.39-91, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01020868

C. Wang, Simultaneous Localization, Mapping and Moving Object Tracking, The International Journal of Robotics Research, vol.26, issue.9, p.38, 2004.
DOI : 10.1177/0278364907081229
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.7969

C. Wang and C. Thorpe, A hierarchical object based representation for simultaneous localization and mapping, p.28, 2004.

C. Wang, . Thorpe, . Charles, . Thrun, . Sebastian et al., Simultaneous Localization, Mapping and Moving Object Tracking, The International Journal of Robotics Research, vol.26, issue.9, pp.889-916, 2007.
DOI : 10.1177/0278364907081229
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.7969

S. Wender, M. Schoenherr, N. Kaempchen, and K. Dietmayer, Classification of laserscanner measurements at intersection scenarios with automatic parameter optimization, IEEE Proceedings. Intelligent Vehicles Symposium, 2005., p.47, 2005.
DOI : 10.1109/IVS.2005.1505084

D. F. Wolf, . Sukhatme, and S. Gaurav, Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments, Autonomous Robots, vol.31, issue.4, pp.53-65, 2005.
DOI : 10.1007/s10514-005-0606-4

J. Xavier, M. Pacheco, D. Castro, A. Ruano, and U. Nunes, Fast Line, Arc/Circle and Leg Detection from Laser Scan Data in a Player Driver. Pages 3930?3935 of: Robotics and Automation, ICRA 2005. Proceedings of the 2005 IEEE International Conference on, p.46, 2005.

H. Zhao and R. Shibasaki, A novel system for tracking pedestrians using multiple singlerow laser-range scanners. Systems, Man and Cybernetics, Part A, IEEE Transactions on, vol.35, issue.46, pp.283-291, 2005.

H. Zhao, X. W. Shao, K. Katabira, and R. Shibasaki, Joint Tracking and Classification of Moving Objects at Intersection Using a Single-Row Laser Range Scanner. Pages 287?294 of: Intelligent Transportation Systems Conference, 2006.

T. Zhao, R. Nevatia, and B. Wu, Segmentation and Tracking of Multiple Humans in Crowded Environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.7, pp.1198-1211, 2008.
DOI : 10.1109/TPAMI.2007.70770

Z. Zivkovic and B. Krose, Part based people detection using 2D range data and images . Pages 214?219 of: Intelligent Robots and Systems, p.47, 2007.