Visual object tracking via collaborative correlation filters

Abstract

Correlation filter (CF) theory has gained a sustainable attractiveness on the tracking field by virtue of its efficiency in training and decision stage. The seminal idea of cyclic shift operations on image patch makes the CF-based trackers accomplish the dense sampling scheme in an efficient manner. Due to all shifted samples merely provide the single appearance of the target in the previous frame, the filters cannot acquire sufficient information of the target appearance, which leads to the tracking failed. To tackle this problem, we designed a novel tracker using collaborative correlation filters, which collaboratively exploit the historical appearances of the target and the surrounding contexts of the previous frame for the filters learning. Therefore, the collaborative correlation filter simultaneously acquires the variations in target appearance and promotes discriminability when the target suffers from occlusion or motion blur. In addition, a unified optimization procedure is proposed to enable abundant information to be embedded in filter learning, which obtains a closed-form solution. Since the historical appearances of the target are collected from previous tracking results and the target appearance may change drastically or suffer from occlusion, using all of these templates for training is inappropriate. To eliminate the useless samples from the template set, we provide a template updating strategy based on collaborative representation. The experimental results on the standard $$\hbox {OTB}100$$ benchmark data sets illustrate the robustness of the proposed tracker.