Vehicle tracking using stochastic fusion-based particle filter

In this article, we propose a new observation model combination approach under particle filtering scheme, which allows robust and accurate visual tracking under typ ical circumstances of real-time visual tracking. This scheme stochastically selects single observation model to evaluate the likelihood of some particle. Since only one single observation likelihood is evaluated for any one particle, the time-cost can be reduced dramatically. To verify its performance, this particle filter is used for vehicle tracking, by stochastically selecting color histogram or edge orientation histogram. The accuracy and robustness of the stochastic fusion approach are evaluated using real sequences. Furthermore, we demonstrate through these experiments that the stochastic fusion scheme performs almost as well as the deterministic fusion approach.