Abstract: Aiming at the problem of target scaling, occlusion and fast movement in target tracking, this paper proposes a self-paced context-aware correlation filter tracking algorithm. First, the global context information of the target is introduced in the regularized least squares classifier so that these context information can be learned by the filter, and a high response to the target and a near-zero response to the context information. Then we introduce self-paced learning, assign weights to the target and context information of each frame, pick out reliable target and context information, and update the filter template. Finally a robust and efficient appearance model is got by learning. Experiments show that the algorithm improves 2.81% in distance accuracy (DP), improves success rate (SR) by 13.9%, and has a good tracking effect.

Key words: target tracking, correlation filtering, context-aware, self-paced learning