Preceding car tracking using belief functions and a particle filter

This article presents a preceding car rear view tracking algorithm which utilizes a particle filter and belief function data fusion. Most of tracking applications resort to only one source of information, making the system dependent on the source reliability. To achieve more robust and longer tracking, multiple source data fusion is a solution. Belief functions are a powerful tool for data fusion. Using bridges between probability theory and belief function theory, data fusion information can be incorporated inside a particle filter. The efficiency of the proposed method is demonstrated on natural on-road sequences.