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Nonparametric data association for particle filter based multi-object tracking : application to multi-pedestrian tracking

Abstract : This article deals with the following issue: how to track a varying number of pedestrians through observations by means of a 4-plane laser sensor. In order to answer to the multiple target tracking problem and more specifically pedestrian tracking, we propose in this paper a statistical approach using a particle filter based on nonparametric data association methods. This approach allows to go beyond the conventional Gaussian assumption and to use as well as possible each particle during track/observation association by means of either a "Parzen Window" kernel method or a K-nearest neighbor algorithm. Simulated and experimental results show the relevance of this method compared to the usual Gaussian window methods.
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https://hal.archives-ouvertes.fr/hal-00344115
Contributor : Paul Checchin <>
Submitted on : Wednesday, December 3, 2008 - 5:09:24 PM
Last modification on : Tuesday, July 2, 2019 - 4:08:03 PM

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  • HAL Id : hal-00344115, version 1

Citation

Samuel Gidel, Christophe Blanc, Thierry Chateau, Paul Checchin, Laurent Trassoudaine. Nonparametric data association for particle filter based multi-object tracking : application to multi-pedestrian tracking. IEEE Intelligent Vehicles Symposium (IV), Jun 2008, Eindhoven, European Union. pp.CD-ROM. ⟨hal-00344115⟩

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