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Aiming to alleviate the visual tracking problem of drift which reduces the abilities of almost all online visual trackers, a robust visual tracker (called CCMM tracker) is proposed with a coupled-classifier based on multiple representative appearance models. The coupledclassifier consists of root and head classifiers based on local sparse representation. The two classifiers collaborate to fulfil a tracking task within the Bayesian-based tracking framework, also to update their templates with a novel mechanism which tries to guarantee an update operation along the "right" orientation. Consequently, the tracker is more powerful in antiinterference. Meanwhile the multiple representative appearance models maintain features of the different submanifolds of the target appearance, which the target exhibited previously. The multiple models cooperatively support the coupled-classifier to recognize the target in challenging cases (such as persistent disturbance, vast change of appearance, and recovery from occlusion) with an effective strategy. The novel tracker proposed in this paper, by explicit inference, can reduce drift and handle frequent and drastic appearance variation of the target with cluttered background, which is demonstrated by the extensive experiments. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers.

A Robust Visual Tracker with a Coupled-Classifier Based on Multiple Representative Appearance Models