Robust kernelized correlation filter with scale adaption for real-time single object tracking

Abstract

Kernelized correlation filter (KCF) is a kind of efficient method for real-time tracking, but remains being challenged by the drifting problem due to inaccurate localization caused by the scale variation and wrong candidate selection. In this paper, we propose a new scale adaptive kernelized correlation filter tracker, termed as SKCF, which estimates an accurate scale and models the distribution of correlation response to address the template drifting problem. In SKCF, a scale adaption method is used to find an accurate candidate. Thus we improve its capacity to drastic scale change which usually happens for unmanned aerial vehicles (UAVs)-based applications. The SKCF also introduces a Gaussian distribution to model the correlation response of the target image to select a better candidate in tracking procedure. Extensive experiments are performed on two commonly used tracking benchmarks and also a new benchmark for UAV tracking with complex scale variations. The results show that the proposed SKCF significantly improves the performance compared to the baseline KCF and achieves better performance than state-of-the-art single object trackers at real-time.