From sample selection to model update: A robust online visual tracking algorithm against drifting

Abstract

This paper proposes an online tracking algorithm that employs a confidence combinatorial map model. Drifting is a problem that easily occurs in object tracking and most of the recent tracking algorithms have attempted to solve this problem. In this paper, we propose a confidence combinatorial map that describes the structure of the object, based on which the confidence combinatorial map model is developed. The model associates the relationship between the object in the current frame and that in the previous frame. On the strength of this relationship, more precisely classified samples can be selected and are employed in the model update stage, which directly influences the occurrence of the tracking drift. The proposed algorithm was estimated on several public video sequences and the performance was compared with several state-of-the-art algorithms. The experiments demonstrate that the proposed algorithm outperforms other comparative algorithms and gives a very good performance.