LAUSR

As one of the key components in many computer vision applications, visual tracking has been widely investigated in recent years. Correlation filter-based trackers have achieved compelling performance in terms of both efficiency and accuracy by speeding up online training and inference procedures in the frequency domain. For a visual object, such trackers often perform tracking by searching the most correlated patch within a limited search region in a new frame. The acknowledged problem is that a small search region prohibits the successful tracking of fast moving targets while a large search region increases the difficulty of model learning. In this paper, we propose to use different candidate search regions to train multiple tracking experts to alleviate this dilemma. Based on these experts, the final tracker learns to make the optimal decision using a decision-theoretic online learning approach. Besides, we also establish multi-scale experts to handle scale variations during tracking. The experimental results on the public datasets demonstrate that the proposed approach achieves favorable performance against several state-of-the-art methods.