Region-filtering correlation tracking

Abstract

Correlation filters (CF) have demonstrated a good performance in visual tracking. However, the base training sample region is larger than the object region, including the interference region (IR). IRs in training samples from cyclic shifts of the base training sample severely degrade the quality of the tracking model. In this paper, a region-filtering correlation tracking (RFCT) algorithm is proposed to address this problem. In this algorithm, we filter training samples by introducing a spatial map into the standard CF formulation. Compared with the existing correlation filter trackers, the proposed tracker has the following advantages. (1) Using a spatial map, the correlation filter can be learned on a larger search region without the interference of IR. (2) Due to processing training samples by a spatial map, it is a more general way to control background information and target information in training samples. In addition, a better spatial map can be explored, the values of which are not restricted. Quantitative evaluations are performed on four benchmark datasets: OTB-2013, OTB-2015, VOT2015, and VOT2016. Experimental results demonstrate that the proposed RFCT algorithm performs favorably against several state-of-the-art methods.