  Discriminative correlation filter (DCF) based trackers have recently achieved
excellent performance with great computational efficiency. However, DCF based
trackers suffer boundary effects, which result in unstable performance in
challenging situations exhibiting fast motion. In this paper, we propose a
novel method to mitigate this side-effect in DCF based trackers. We change the
search area according to the prediction of target motion. When the object moves
fast, broad search area could alleviate boundary effects and reserve the
probability of locating the object. When the object moves slowly, narrow search
area could prevent effect of useless background information and improve
computational efficiency to attain real-time performance. This strategy can
impressively soothe boundary effects in situations exhibiting fast motion and
motion blur, and it can be used in almost all DCF based trackers. The
experiments on OTB benchmark show that the proposed framework improves the
performance compared with the baseline trackers.
