Robust Object Tracking via Large Margin and Scale-Adaptive Correlation Filter

Abstract

Due to the impressive performance and computational efficiency of correlation filter (CF)-based object tracking methods, CF trackers have gained lots of popularity in recent years. However, target drift and tracking failure caused by background clutter and target appearance change (resulting from scale variation and deformation and so on) are still challenging tasks. To overcome these challenges, we propose a new tracking method within the CF framework in this paper. First, we learn a large margin CF by exploiting discriminative background patches. Contrary to conventional CF trackers that aim to maximize target response, we model a tracker that maximizes the margin between the target and surrounding background by exploiting background information effectively. To remedy the deficiency in handling target scale variation of CF-based trackers, we propose to train a CF by multi-level scale supervision, which aims to make CF sensitive to the target scale variation. Then, we integrate the two individual modules into one framework to simplify our tracking model. The proposed method can effectively prevent tracking module degradation introduced by target appearance changes. Extensive experiments conducted on public available data sets OTB-50/100 demonstrate that the proposed tracking method is robust to the background clutter and discriminative to the target scale variation. Both qualitative and quantitative results show the excellent performance against some state-of-the-art trackers.