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A robust correlation filter tracking method based on adaptive model updating

Correlation filter-based target tracking is a simple and efficient method for visual tracking. However, the correlation filter tracker updates the target model with a fixed learning rate which cannot adapt to the change of target appearance for a real-time tracking, resulting in model drift and tracking failure. To deal with these issues, we develop an adaptive model updating method based on the peak-to-sidelobe ratio (PSR) of response map and the mean value of the frame difference. We adjust the learning rate according to different scenarios to improve the target tracking performance. Extensive experimental results conducted on OTB2013 and OTB2015 prove that the proposed FSAMF tracker superiority over others state-of-the-art trackers.