Object-level fusion and confidence management in a multi-sensor pedestrian tracking system

This paper describes a multi-sensor fusion system dedicated to detect, recognize and track pedestrians. The fusion by tracking method is used to fuse asynchronous data provided by different sensors with complementary and supplementary fields of view. The confidence in detection and recognition is calculated based in geometric features and it is updated using the transferable belief model framework. The vehicle proprioceptive data are filtered by a separate Kalman filter and are used to estimate the relative and the absolute state of detected pedestrians. Results are shown with experimental data acquired in urban environment.

[1]  Véronique Berge-Cherfaoui,et al.  Distributed data fusion: application to confidence management in vehicular networks , 2008, 2008 11th International Conference on Information Fusion.

[2]  Hugh F. Durrant-Whyte,et al.  Process models for the high-speed navigation of road vehicles , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[3]  T. Denœux.  Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence , 2008 .

[4]  Christophe Debain,et al.  A real-time, multi-sensor architecture for fusion of delayed observations: application to vehicle localization , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[5]  P. Smets,et al.  Assessing sensor reliability for multisensor data fusion within the transferable belief model , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Thierry Denoeux.  The cautious rule of combination for belief functions and some extensions , 2006, 2006 9th International Conference on Information Fusion.

[7]  Robert W. Sittler,et al.  An Optimal Data Association Problem in Surveillance Theory , 1964, IEEE Transactions on Military Electronics.

[8]  Dominique Meizel,et al.  Dynamic Localization of Car-like Vehicles using Data Fusion of Redundant ABS Sensors , 2003, Journal of Navigation.

[9]  Véronique Cherfaoui,et al.  Detection and Recognition confidences update in a multi-sensor pedestrian tracking system , 2008 .

[10]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

[11]  V. Cherfaoui,et al.  Updating confidence indicators in a multi-sensor pedestrian tracking system , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[12]  K. Fuerstenberg.  Pedestrian Detection and Classification by Laserscanners , 2003 .

[13]  V. Cherfaoui,et al.  Tracking objects using a laser scanner in driving situation based on modeling target shape , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[14]  Philippe Smets,et al.  The Transferable Belief Model , 1991, Artif. Intell..

[15]  Philippe Smets,et al.  The Transferable Belief Model , 1994, Artif. Intell..

[16]  F. Suard,et al.  Pedestrian detection using stereo-vision and graph kernels , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[17]  J. J. Sudano.  Inverse pignistic probability transforms , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).