Object tracking by multi-cues spatial pyramid matching

In this paper, we propose a novel tracking framework, multi-cues spatial pyramid matching (MSPM). Different cues are used to generate a set of probability maps, where the value of each pixel indicates the probability that it belongs to the foreground. Then those probability maps are combined into a single probability map by a weighted linear function. There exist two main contributions. First, a generic probability maps fusion mechanism is proposed. The weights of different probability maps are updated dynamically to maintain local discriminative power, which is achieved by solving a regression problem efficiently. Second, spatial pyramid matching kernel is adopted as a likelihood function, which considers spatial information of object and is able to cope with occlusions naturally. Experiments performed on several challenging public video sequences demonstrate that our proposed framework achieves considerable performance, compared to algorithms with individual cues or equal weights combination, and other state-of-the-art ones.