Discriminative visual tracking via spatially smooth and steep correlation filters

Recently, many discriminative correlation filter (DCF)-basedapproaches have been developed to alleviate the undesired boundary effects of traditional DCF-based methods. However, these approaches either focus on the target area by suppressing the background information or exploit a spatial regularisation term to penalise filter coefficients far from the target centre, where real negative samples are exceedingly rare and irrelevant information is integrated into filters. Based on the observation that the tracking model is strongly dependent on the quality of training samples, this paper proposes a novel DCF-based tracker to precisely process the training set through a smooth and steeply decreasing function in two respects. On the one hand, the surrounding information is suitably incorporated into the target. On the other hand, every real background patch is collected as a negative sample. In addition, the target appearance model is adaptively updated by achieving an appropriate trade-off between current and historical models according to their reliability. Experimental results show that our method outperforms other state-of-the-art trackers in terms of accuracy and efficiency. In particular, compared with the baseline tracker, our method achieves gains of 7.4% and 9.9% in terms of the area under the curve (AUC) on the OTB-100 and LaSOT datasets, respectively.