Tracking algorithm using background-foreground motion models and multiple cues

We present a stochastic tracking algorithm for surveillance videos where targets are dim and of low resolution. Our tracker is mainly based on the particle filter algorithm. Two important novel features of the tracker include: A motion model consisting of both background and foreground motion parameters is built. Multiple cues are adaptively integrated in a system observation model when estimating the likelihood functions. Based on these features, the accuracy and robustness of the tracker has been improved, which is very important for surveillance problems. We present the results of applying the proposed algorithm to many videos.