Learning Robust Gaussian Process Regression for Visual Tracking

Recent developments of Correlation Filter based trackers (CF trackers) have attracted much attention because of their top performance. However, the boundary effect imposed by the basic periodic assumption in their fast optimization seriously degrades the performance of CF trackers. Although there existed many recent works to relax the boundary effect in CF trackers, the cost was that they can not utilize the kernel trick to improve the accuracy further. In this paper, we propose a novel Gaussian Process Regression based tracker (GPRT) which is a conceptually natural tracking approach. Compared to all the existing CF trackers, the boundary effect is eliminated thoroughly and the kernel trick can be employed in our GPRT. In addition, we present two efficient and effective update methods for our GPRT. Experiments are performed on two public datasets: OTB-2013 and OTB-2015. Without bells and whistles, on these two datasets, our GPRT obtains 84.1% and 79.2% in mean overlap precision, respectively, outperforming all the existing trackers with hand-crafted features.