Correlation Filters With Adaptive Multiple Contexts for Visual Tracking

The local contexts define the target and its surrounding background within a constrained region, and have been proved useful for visual tracking, but how to adaptively employ them for building robust models remains challenging. By using the spatial weight maps, the correlation filter (CF) methods with spatial regularization provide an alternative to exploit the local contexts for appearance modeling. However, they generally utilize naive spatial weight map functions, and fail to flexibly regulate the effects of the target and background on model learning, thereby restricting the tracking performance. In this paper, we address these issues by presenting an adaptive multiple contexts correlation filter (AMCCF) framework. In particular, a novel sigmoid spatial weight map is first proposed to control the impacts of local contexts for learning more effective CF models. Based on this, different levels of local contexts (multiple contexts) are further modeled by incorporating the spatial weight maps with different parameters into multiple CF models. To adaptively utilize the local contexts on the tracking stage, the minimal weighted confidence margin loss function with a weight prior constraint is adopted for jointly estimating the target position and adaptive fusion weights of response maps from different CF models. To validate the proposed method, extensive experiments are conducted on four tracking benchmarks. The results show that our AMCCF can adaptively leverage the local contexts for robust tracking, and performs favorably against the state-of-the-art trackers.