Robust object tracking based on online update of multi-feature template

This paper proposes a robust object tracking algorithm under the Mean-shift framework based on the online update strategy of multi-feature template. At first, to solve the drift problem caused by cluttered backgrounds, the illumination invariant color features and the rotation invariant LBP texture feature were extracted and were combined together with the BWH algorithm. Secondly, in addition to the traditional convergence condition of Mean-shift algorithm, a histogram similarity checking step was presented against the local optima problem. Besides, occlusion detection algorithm based on spatial distribution of the histogram difference was proposed to enhance the precision of the template update. Experimental results showed that the proposed tracking algorithm is robust and accurate against cluttered dynamical background, occlusion and the object deformation.