Spatial Adaptive Regularized Correlation Filter for Robust Visual Tracking

Abstract

Correlation filter is a simple yet efficient method to deal with the visual tracking task. However, the unwanted boundary effects hinder further performance improvement. Spatially Regularized DCF (SRDCF) has been proposed to address this problem with a pre-computed spatial penalty matrix, which improves the tracking performance greatly. In this paper, aiming to achieve more accurate spatial regularization, we present our spatial adaptive regularized correlation filter (SARCF). A coarse-to-fine scale estimation approach is proposed to change the spatial penalty area, which can efficiently deal with large scale variation. Moreover, temporal regularization is introduced for long-term tracking. Experimental results show that the proposed algorithm outperforms most advanced algorithms in tracking accuracy and success rate.