C. Coué, T. Fraichard, P. Bessiere, and E. Mazer, Multi-sensor data fusion using Bayesian programming: An automotive application, Intelligent Vehicle Symposium, 2002. IEEE, 2002.
DOI : 10.1109/IVS.2002.1187989

C. Coue, T. Fraichard, P. Bessiere, and E. Mazer, Using bayesian programming for multi-sensor multitarget tracking in automotive applications, Proc. Int'l. Conf. Robotics and Automation, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00068773

K. Mekhnacha and E. M. Bessiere, The design and implementation of a Bayesian CAD modeler for robotic applications, Advanced Robotics, vol.9, issue.4, pp.45-70, 2001.
DOI : 10.1163/156855301750095578

URL : https://hal.archives-ouvertes.fr/hal-00019182

K. O. Arras, N. Tomatis, and R. Siegwart, Multisensor on-the-fly localization:, Robotics and Autonomous Systems, vol.34, issue.2-3, pp.131-143, 2001.
DOI : 10.1016/S0921-8890(00)00117-2

A. H. Jazwinsky, Stochastic processes and filtering theory, 1970.

Y. Bar-shalom and W. D. Blair, Multitarget-multisensor tracking: application and advances, 2000.

H. Gauvrit, J. P. Cadre, and C. Jauffret, A formulation of multitarget tracking as an incomplete data problem, IEEE Transactions on Aerospace and Electronic Systems, vol.33, issue.4, pp.1242-1257, 1997.
DOI : 10.1109/7.625121

S. S. Blackman and R. Popoli, Design and analysis of modern tracking systems, 1999.

C. Wang, C. Thorpe, and S. Thrun, Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas, Proc. Int'l. Conf. Robotics and Automation, pp.842-849, 2003.

H. Moravec, Sensor Fusion in Certainty Grids for Mobile Robots, AI Magazine, vol.9, pp.61-74, 1988.
DOI : 10.1007/978-3-642-74567-6_19

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

E. Prassler, J. Scholz, and A. Elfes, Tracking multiple moving objects for real-time robot navigation, Journal of Autonomous Robots, Special Issue on Perception for Mobile Agents, vol.8, 2000.

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

P. F. Felzenszwalb and D. P. Huttenlocher, Pictorial Structures for Object Recognition, International Journal of Computer Vision, vol.61, issue.1, pp.55-79, 2005.
DOI : 10.1023/B:VISI.0000042934.15159.49

D. Schulz, W. Burgard, D. Fox, and A. B. Cremers, People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters, The International Journal of Robotics Research, vol.22, issue.2, pp.99-116, 2003.
DOI : 10.1177/0278364903022002002

C. Rasmussen and G. D. Hager, Probabilistic data association methods for tracking complex visual objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.6, pp.560-576, 2001.
DOI : 10.1109/34.927458

J. A. Roecker and G. L. Phillis, Suboptimal joint probabilistic data association, IEEE Transactions on Aerospace and Electronic Systems, vol.29, issue.2, pp.510-517, 1993.
DOI : 10.1109/7.210087

. Fig, Together with Fig.2. Tracking sequence based on the data from CAVIAR. The first column shows the input image with the bounding boxes indicating detected humans. Second column and third column shows the corresponding representation of the BOF and JPDA tracker respectively