A structural coupled-layer tracking method based on correlation filters

A recent trend in visual tracking is to employ correlation filter based formulations for their high efficiency and superior performance. To deal with partial occlusion issue, part-based methods via correlation filters have been introduced to visual tracking and achieved promising results. However, these methods ignore the intrinsic relationships among local parts and do not consider the spatial structure inside the target. In this paper, we propose a coupled-layer tracking method based on correlation filters that resolves this problem by incorporating structural constraints between the global bounding box and local parts. In our method, the target state is optimized jointly in a unified objective function taking into account both appearance information of all parts and structural constraint between parts. In that way, our method can not only have the advantages of existing correlation filter trackers, such as high efficiency and robustness, and the ability to handle partial occlusion well due to part-based strategy, but also preserve object structure. Experimental results on a challenging benchmark dataset demonstrate that our proposed method outperforms state-of-art trackers.