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Adaptive distractor-aware for siamese tracking via enhancement confidence evaluator

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Abstract

Siamese networks have recently attracted much attention due to balancing accuracy and speed in tracking. However, most Siamese trackers emphasize how to learn target features, ignoring background information. In this paper, a novel Siamese-based method via an adaptive distractor-aware and enhancement confidence evaluator is proposed. First, to make full use of the background information, a distractor model is designed, which is used to select the dominant feature channels of less interference for target representation. Additionally, to enable the target model to work well with challenging scenes, an adaptive weights-aware strategy is proposed, which can adjust the feature channel weights online according to the single-convolutional attention network with L2 loss. In addition, a dynamic template update strategy is proposed to adapt target appearance variations. The strategy evaluates the tracking results via a new correlation peak average energy (CPAE) evaluator, which has more confidence for good templates, guiding the fusion of reliable template samples and further enhancing the adaptability of the template and making the tracker more robust. Experimental results on widely used OTB100, TC-128, UAV123, VOT2016 and VOT2018 benchmarks verified that the proposed tracker achieved outstanding performance compared with state-of-the-art trackers.

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Data Availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant (62272423, 62102373, 62006213, 62072416, 61806181), Program for Science & Technology Innovation Talents in Universities of Henan Province, China (21HASTIT028), Natural Science Foundation of Henan Province, China (202300410495), Zhongyuan Science and Technology Innovation Leadership Program, China (214200510026).

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  1. Huanlong Zhang and Linwei Zhu contributed equally to this work.

Authors and Affiliations

  1. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Dongfeng Road, Zhengzhou, 450002, Henan Province, People’s Republic of China

    Huanlong Zhang, Linwei Zhu, Huaiguang Wu & Jianwei Zhang

  2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Yatai Road, Huzhou, 313001, Zhejiang Province, People’s Republic of China

    Yanchun Zhao

  3. College of Engineering, Northeastern University, Boston, MA, 02115, USA

    Yingzi Lin

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  1. Huanlong Zhang
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Zhang, H., Zhu, L., Wu, H. et al. Adaptive distractor-aware for siamese tracking via enhancement confidence evaluator. Appl Intell 53, 29223–29241 (2023). https://doi.org/10.1007/s10489-023-05068-4

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