Robust kernel correlation filter tracker based on adaptive scale and occlusion detection

Xuewei Fang; Huabin Wang; Liang Tao; Xian Wang

doi:10.1117/12.2589374

27 January 2021 Robust kernel correlation filter tracker based on adaptive scale and occlusion detection

Xuewei Fang, Huabin Wang, Liang Tao, Xian Wang

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117200H (2021) https://doi.org/10.1117/12.2589374
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Correlation filter-based trackers exploit large numbers of cyclically shifted samples to train the object classifier, which can achieve good results in tracking accuracy and speed. However, when in complex scenes such as occlusion or deformation, tracking drift or loss will occur. In this paper, a kernel correlation filter tracker base on scale adaptive and occlusion detection is proposed to strengthen the tracker robustness. Firstly, a robust appearance model combine the gradient feature and color feature is proposed to enhance the features representation ability; Secondly, a scale adaptive mechanism is introduced to handle the problem of the fixed template size, and the Newton method is used to find the maximum response value to more accurate predict the center position of the target and estimate the target scale; Finally, the occlusion detection scheme adopted when update model to avoid tracking failure due to appearance model pollution. Experiments are performed on the OTB2013 Benchmark Dataset, the results show that, compared to the basic tracker, we obtain an absolute gain of 6.6% and 13.4% respectively in mean distance precision and mean overlap precision.

Citation Download Citation

Xuewei Fang, Huabin Wang, Liang Tao, and Xian Wang "Robust kernel correlation filter tracker based on adaptive scale and occlusion detection", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117200H (27 January 2021); https://doi.org/10.1117/12.2589374

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