Region-filtering Correlation Tracking

TL;DR

The paper introduces RFCT, a novel correlation tracking method that filters training samples using a spatial map to improve tracking accuracy by reducing interference from irrelevant regions.

Contribution

The paper proposes a new region-filtering approach in correlation tracking that enhances model robustness by controlling background and target information through a spatial map.

Findings

RFCT outperforms several state-of-the-art trackers on OTB benchmarks.

Filtering training samples improves tracking accuracy and robustness.

The spatial map approach effectively reduces interference from irrelevant regions.

Abstract

Recently, correlation filters have demonstrated the excellent performance in visual tracking. However, the base training sample region is larger than the object region,including the Interference Region(IR). The IRs in training samples from cyclic shifts of the base training sample severely degrade the quality of a tracking model. In this paper, we propose the novel Region-filtering Correlation Tracking (RFCT) to address this problem. We immediately filter training samples by introducing a spatial map into the standard CF formulation. Compared with existing correlation filter trackers, our proposed tracker has the following advantages: (1) The correlation filter can be learned on a larger search region without the interference of the IR by a spatial map. (2) Due to processing training samples by a spatial map, it is more general way to control background information and target information in training samples. The values of the spatial map are not restricted, then a better spatial map can be explored. (3) The weight proportions of accurate filters are increased to alleviate model corruption. Experiments are performed on two benchmark datasets: OTB-2013 and OTB-2015. Quantitative evaluations on these benchmarks demonstrate that the proposed RFCT algorithm performs favorably against several state-of-the-art methods.