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Siamese object tracking based on multi-frequency enhancement feature

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Abstract

Tracker based on the Siamese network is an important research direction of target tracking. However, Siamese trackers have the problem of insufficient characteristic expression ability extensively. To improve the situation, we introduced the effective attention module multi-frequency attention, which considers the attention mechanism from the frequency domain perspective and effectively raises the efficiency of feature representation of the tracker. Meanwhile, densely connected neural networks are applied to the tracker, which integrates the surface orientation information and deep-seated semantic features about the object, which will contribute to enhancing the localization and expression ability of the tracker. The proposed method achieves excellent results on Siamese network tracking. In order to prove the effectiveness of our strategy, we conducted experiments on benchmarks, including OTB100, OTB2013, VOT2016, and VOT2018, and the obtained results indicate that strategy we proposed achieves high accuracy and efficiency and still keeps outstanding with the effect of occlusion and illumination. Experiments show that the proposed method fulfills real-time tracking.

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Data availability

The data used to support the findings of this study have been deposited in the [The Visual Object Tracking VOT2016 Challenge Results and The Sixth Visual Object Tracking] repository ([https://doi.org/10.1007/978-3-319-48881-3_54] [https://doi.org/10.1007/978-3-030-11009-3_1]).

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Funding

Funding was provided by National Key Research and Development Program of China. Grant Numbers (2020YFB1712401, 2018******4402).

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450000, China

    Haibo Pang, Linxuan Han, Chengming Liu & Rongqi Ma

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  1. Haibo Pang
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  2. Linxuan Han
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Correspondence to Chengming Liu.

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Pang, H., Han, L., Liu, C. et al. Siamese object tracking based on multi-frequency enhancement feature. Vis Comput 40, 261–271 (2024). https://doi.org/10.1007/s00371-023-02779-0

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