Robust tracking via weighted spatio-temporal context learning

Abstract

Designing a robust visual tracker is a challenging problem due to many disturbed factors such as illumination changes, appearance changes, rotation, partial or full occlusions, etc. Among numerous existed trackers, correlation filter based tracker is a fast and robust method with resistance to the above-mentioned factors. Motivated by that, spatio-temporal context (STC) learning algorithm is proposed, which considers the information of the context around the target and achieved better performance. However, STC treats the whole region of the context equally, which weakens the effectiveness of the context information. In this paper, we propose a novel weighted spatio-temporal context (WSTC) learning algorithm. Our algorithm considers the surrounding context discriminatively and integrates a weighted map by evaluating the importance of different regions. Extensive experimental results on various benchmark databases show that our algorithm outperforms the STC algorithm and the other state-of-the-art algorithms.