Weak-structure-aware visual object tracking with bottom-up and top-down context exploration

It poses great challenges to model-free trackers that the object undergoes large appearance variations due to motion, shape deformation, occlusion and surrounding environments. In this paper, we investigate a novel method for modeling and locating the object by being aware of the weak structures of discriminative parts of both the object and its surroundings. The discriminative parts are modeled based on keypoints and feature descriptors. We separate the discriminative parts into two sets corresponding to object and background, and model their spatial structure relationship with the object. While tracking, the successfully localized parts will contribute to potential centers of the object. Aware of the weak structures, we further cluster potential centers to locate the object. The object scale is also updated adaptively. To increase the accuracy of this weak-structure-aware location inference, we fully explore context in both bottom-up and top-down procedures. In the bottom-up stage, we explore the local motion estimation of low-level pixels. The bottom-up information produces consistent tracking of discriminative parts. In the top-down stage, we build a superpixel kernel model to roughly distinguish the object from its surroundings, which provides guided information for location inference and model update. The effectiveness of the proposed method is verified by evaluation on a popular benchmark and comparison with recent tracking methods.