A robust correlation filter tracking method based on adaptive model updating

Lin Zhang; Xingzhong Xiong; Xin Zeng; Ya Dong

doi:10.1117/12.2622718

1 December 2021 A robust correlation filter tracking method based on adaptive model updating

Lin Zhang, Xingzhong Xiong, Xin Zeng, Ya Dong

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120791L (2021) https://doi.org/10.1117/12.2622718
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

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.

Citation Download Citation

Lin Zhang, Xingzhong Xiong, Xin Zeng, and Ya Dong "A robust correlation filter tracking method based on adaptive model updating", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120791L (1 December 2021); https://doi.org/10.1117/12.2622718

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KEYWORDS

Image filtering

Electronic filtering

Optical tracking

Video

Digital filtering

Detection and tracking algorithms

Filtering (signal processing)

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