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Adaptive Correlation Filters Feature Fusion Learning for Visual Tracking

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Book cover Artificial Neural Networks and Machine Learning – ICANN 2021 (ICANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12895))

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Abstract

Tracking algorithms based on discriminative correlation filters (DCFs) usually employ fixed weights to integrate feature response maps from multiple templates. However, they fail to exploit the complementarity of multi-feature. These features are against tracking challenges, e.g., deformation, illumination variation, and occlusion. In this work, we propose a novel adaptive feature fusion learning DCFs-based tracker (AFLCF). Specifically, AFLCF can learn the optimal fusion weights for handcrafted and deep feature responses online. The fused response map owns the complementary advantages of multiple features, obtaining a robust object representation. Furthermore, the adaptive temporal smoothing penalty adapts to the tracking scenarios with motion variation, avoiding model corruption and ensuring reliable model updates. Extensive experiments on five challenging visual tracking benchmarks demonstrate the superiority of AFLCF over other state-of-the-art methods. For example, AFLCF achieves a gain of 1.9\(\%\) and 4.4\(\%\) AUC score on LaSOT compared to ECO and STRCF, respectively.

This work was supported by the National Key Research and Development Program of China under Grants 2019YFB2101904, the National Natural Science Foundation of China under Grants 61732011 and 61876127, the Natural Science Foundation of Tianjin under Grant 17JCZDJC30800, and the Applied Basic Research Program of Qinghai under Grant 2019-ZJ-7017.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Hongtao Yu & Pengfei Zhu

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  1. Hongtao Yu
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  2. Pengfei Zhu
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Correspondence to Pengfei Zhu .

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Editors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. iMotions A/S, Copenhagen, Denmark

    Paolo Masulli

  3. University of Tübingen, Tübingen, Baden-Württemberg, Germany

    Sebastian Otte

  4. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

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Yu, H., Zhu, P. (2021). Adaptive Correlation Filters Feature Fusion Learning for Visual Tracking. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12895. Springer, Cham. https://doi.org/10.1007/978-3-030-86383-8_52

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  • DOI: https://doi.org/10.1007/978-3-030-86383-8_52

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