Adaptive context-aware correlation filter tracker with multiple detection regions

Lei Pu; Xinxi Feng; Zhiqang Hou; Wangsheng Yu

doi:10.1117/12.2589424

27 January 2021 Adaptive context-aware correlation filter tracker with multiple detection regions

Lei Pu, Xinxi Feng, Zhiqang Hou, Wangsheng Yu

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117200L (2021) https://doi.org/10.1117/12.2589424
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Correlation filter based tracking algorithms have recently shown favorable performance in terms of high frame rates. However, a significant problem is that the context information is not be fully used which can result in model drift under challenging situations, such as fast motion and occlusion. In this paper, we propose an adaptive context-aware correlation framework which can improve the discriminative power and detect target within a large neighborhood. Firstly, we construct a context-aware correlation filter model and a peak extraction method is proposed to select the context patches adaptively, which can be regarded as hard negative samples mining. Secondly, a simple yet effective multi-region detection strategy is proposed to improve the anti-occlusion ability and prevent model drift. Thirdly, we adopt high-confidence model update method to avoid model corruption. We integrate the proposed framework with the existing DCF tracker, experimental results show that the proposed framework improves the accuracy by 9.1% and the success rate by 7.1%.

Citation Download Citation

Lei Pu, Xinxi Feng, Zhiqang Hou, and Wangsheng Yu "Adaptive context-aware correlation filter tracker with multiple detection regions", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117200L (27 January 2021); https://doi.org/10.1117/12.2589424

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