Object tracking based on adaptive multi-cue integration mean shift

A tracking algorithm based on adaptive multi-cue integration mechanism is proposed. The RGB color cue and local binary pattern (LBP) texture cue are utilized to represent the target, and then they are combined by linear weighting to the similarity function. By expanding the similarity function, a new expression consisting of the data items with local information is obtained, and mean shift algorithm is used to find out the optimal location by iterative computation. Sigmoid kernel are used to adjust the feature weight adaptively in the tracking procedure, Bhattacharyya coefficient and reliability index are used as criterions for selective sub-model update. Experimental results show that the tracker based on multi-cue integration mean shift works even more robustly. With adaptive multi-cue integration mechanism and selective model update strategy, the problem of tracking failures caused by using single cue or model drift in complex scenes can be solved.