Fast tracking via context depth model learning

Visual tracking is one of the challenging tasks in computer vision. In this paper, we propose a fast and robust visual tracking algorithm which is directly extended from STC [1]. By exploring RGB-D data, we construct a context depth model to record spatial correlation between the low-level features from the target and its surrounding regions. According to the continuity and stability of target in depth image, we adopt region growing method and a model updating schema for scaling and occlusion detection. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.