A Robust Visual Tracking Method Based on Correlation Filter

Abstract Although the fast discriminative scale space tracking(fDSST) method shows superior performance for short time visual tracking, it is prone to tracking failure when the target is occluded or moving fast in case of long time tracking. To address this issue, we proposed a novel enhanced visual tracking method based on fDSST for robust tracking. Specifically, based on correlation filter response map we design a visual tracking status discrimination method by integrating Peak to Sidelobe Ratio(PSR) and the number of response peaks. Then, we design an adaptive model update method coupled with extended search area strategy to reduce the probability of target loss. Extensive experiments are performed on challenging benchmark sequences from Online Object Tracking Benchmark(OTB) with significant target occlusion and fast motion. Ours results show that the proposed approach improves the DP by 11.9% and AUC by 8.4% compared to the baseline fDSST, and operates at real-time.