A robust approach for multiple vehicles tracking using layered particle filter

Multiple vehicle targets tracking is one of the most challenging problems in Intelligent Transportation Systems. It is used for recognizing and understanding vehicle behaviors, especially suffering from illumination, scale, pose variations and occlusions. In this paper, we explore a robust tracking algorithm, combining deterministic and probabilistic methods, to solve this problem. We build a fusion observation model with color and local integral orientation descriptor, and give multiple vehicle targets model. In order to overcome the disadvantage of particle impoverishment, we propose a layered particle filter architecture embedding continuous adaptive mean shift, which considers both concentration and diversity of particles, and the particle set can better represent the posterior probability density. This paper also presents experiments using real video sequences to verify the proposed method.