Collaborative Correlation Filter Tracking with Online Re-detection

Abstract

Visual object tracking is a challenging task in computer vision. Recently, due to high efficiency and performance, correlation filter-based trackers have attracted much attention. In this paper, we propose a collaborative correlation filter tracking framework. First, we learn multiple kernelized correlation filters for different features independently, and fuse their response maps to obtain a more reliable response map for translation estimation. Then, we learn a scale correlation filter to handle the scale variation. Moreover, in order to further improve the tracking accuracy, we build an online detector to re-detect objects in local neighbor region, when the tracking result is unreliable. Extensive evaluations on the recent benchmark datasets show that the proposed algorithm performs favorably against several state-of-the-art methods.