Robust mean shift tracking based on refined appearance model and online update

Abstract

In this paper, a robust mean shift tracking algorithm based on refined appearance model and online update strategy is proposed. The main idea of the proposed algorithm is to construct a more accurate appearance model and design an online update strategy. At the beginning of the tracking, the simple mean shift tracking algorithm is applied on the first few frames to collect a set of target templates, which contains both foreground and background of the target. During the model con- struction, simple linear iterative clustering (SLIC) algorithm is exploited to obtain the superpixels of the target templates, and the superpixels are further clustered to classify the background from foreground. A weighted vector is then obtained based on the classified background and foreground, which is utilized to modify the kernel histogram appearance model. The following frames are processed based on the mean shift tracking algorithm with the modified appearance model, and the stable tracking results with no occlusion will be selected to update the appearance model. The concrete operation of model update is the same as model construction. Experiment results on challenging test sequences indicate that the proposed algorithm can well cope with both appearance variation and background change to obtain a robust tracking performance.