Particle Filter with Improved Proposal Distribution for Vehicle Tracking

Authors:
Huaping Liu;Fuchun Sun
Affiliations:
Department of Computer Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing, P.R. China;Department of Computer Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing, P.R. China
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

Symmetry is an important characteristic of vehicles and has been frequently used for detection tasks by many researchers. However, existing results of vehicle tracking seldom used symmetry property. In this paper, we will utilize the detected symmetry feature to design a proposal distribution of particle filter for vehicle tracking. The resulting proposal distribution can be closer to the true posterior distribution. Experimental results show that the use of symmetry information will obtain better tracking performance than the conventional color histogram-based particle filters.