Tracking Algorithm Using Background-Foreground Motion Models and Multiple Cues

We present a stochastic tracking algorithm for surveillance videoswhere targets are dim and of low resolution. Our tracker is mainly
based on the particle filter algorithm. Two important novel fea-
tures 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.