Visual tracking based on object appearance and structure preserved local patches matching

Drift is the most difficult issue in object visual tracking based on framework of “tracking-by-detection”. Due to the self-taught learning, the mis-aligned samples are potentially to be incorporated in learning and degrade the discrimination of the tracker. This paper proposes a new tracking approach that resolves this problem by three multi-level collaborative components: a high-level global appearance tracker provides a basic prediction, upon which the structure preserved low-level local patches matching helps to guarantee precise tracking with minimized drift. Those local patches are deliberately deployed on the foreground object via foreground/background segmentation, which is realized by a simple and efficient classifier trained by super-pixel segments. Experimental results show that the three closely collaborated components enable our tracker runs in real time and performs favourably against state-of-the-art approaches on challenging benchmark sequences.