Robust Visual Tracking via Dirac-Weighted Cascading Correlation Filters

Abstract

Correlation filter-based trackers (CFTs) have recently raised considerable attention in visual tracking and achieved competitive performance. Nevertheless, conventional structures of such CFTs build a shallow architecture with a single correlation filter, which cannot comprehensively depict the target appearance. Hence, these trackers lack strong discriminative ability and easily drift when the target suffers drastic appearance variations. To address the limitations, we propose Dirac-weighted cascading correlation filters (DWCCF) for visual tracking. It incorporates cascading characteristics of multiple filters to construct the target appearance model, and dynamically learns Dirac weights for each filter, which is accordingly robust to appearance variations. Besides, we design a boundary penalization strategy to adaptively reduce the boundary effects, which efficiently improves the detection precision for tracking. Qualitative and quantitative evaluations on OTB-2013 and OTB-2015 datasets demonstrate that the proposed DWCCF significantly outperforms other state-of-the-art methods.