SOFT MASK CORRELATION FILTER FOR VISUAL OBJECT TRACKING

Correlation filters have shown excellent performance in visual object tracking both in speed and accuracy. However, the traditional correlation filters learn from the shifted patches rather than real background patches, which may reduce the discrimination in challenging situations. In this paper, we propose a Soft Mask Correlation Filter (SMCF) which can effectively model the object by real image patches. The soft mask is conducted on the entire frame densely and crops real background patches for training. It enables the correlation filter to pay more attention to the center part of the object rather than an axis aligned rectangle which contains background pixels. Both quantitative and qualitative evaluations conducted on tracking benchmarks demonstrate the superior performance of our method compared to the state-of-the-art trackers.