Robust kernelised correlation filter tracking with least absolute deviations regression

Abstract

Kernelised correlation filter (KCF) has demonstrated its superior performance in visual tracking. The strength of the approach stems from its ability to efficiently learn the object appearance variations over time. A fundamental drawback to KCF, however, is that the objective function is modelled as a ridge regression problem that is sensitive to outliers, thereby resulting in suboptimal performance when the target appearance during tracking suffers from severe variations due to illumination variation, partial occlusion, background clutter, and so on. To address this issue, the KCF as a least absolute deviations regression (LADR) that is robust to outliers is reformulated, and then an efficient and effective approach to solve it via the alternating direction method of multipliers (ADMM) is presented. The ADMM splits the LADR into a ridge regression problem as the conventional KCF and a soft-thresholding one, and hence the proposed approach is still extremely efficient as the conventional KCF. Evaluations are conducted on the CVPR2013 tracking benchmark dataset with 50 sequences, which demonstrate the superior performance of the proposed method over the state-of-the-art trackers.