Background-Suppressed Correlation Filters for Visual Tracking

Zhihao Chen, Qing Guo, Liang Wan, Wei Feng

Abstract: Correlation filters (CF) visual object tracking is a powerful framework, with excellent tracking accuracy and beyond real-time frame rate. Its performance, however, can be severely degraded in cluttered background images. In this paper, we propose background-suppressed correlation filters (BSCF), a better CF tracking scheme, which can significantly improve the reliability and accuracy of CF trackers, without harming their beyond real-time speed. Specifically, we present a unified BSCF object function. We show that both the correlation filters and BS weight map can be efficiently and jointly solved in frequency domain. Extensive experiments on OTB-100 benchmark validate the effectiveness and generality of BS in improving multiple CF trackers with higher accuracy and robustness while maintaining their fast tracking speed. We also show BS boosted CF tracker can achieve comparable accuracy of the state-of-the-art spatially-regularized CF tracker but is 14 times faster.