Dynamic multi-cue tracking using particle filter

We present a novel particle filter approach in this paper for robust object tracking using multi-cue association. In contrast to conventional particle trackers that are based on the single space, which does not provide enough information of good foreground/background discrimination, mapping the object into multiple spaces can help to seize the most informative properties of distinguishing the target from background. Moreover, the proposed algorithm commands all the spaces and their corresponding judgments in a global boosting view, which is implemented in an incremental manner to strengthen the ability of separating the target and background classes. The implicit appearance model is then used in updating step of the particle filter to measure the target region. Experimental results on several challenging video sequences have verified that the proposed method is compared very robust and effective with the traditional particle filter in many complicated scenes.