  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.
