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Parallel Attention Mechanism Based Multi-feature Fusion for Underwater Object Tracking

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Artificial Intelligence and Robotics (ISAIR 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1998))

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Abstract

Recently, siamese network-based trackers have achieved great success, however underwater object tracking has been rarely studied. In underwater environments, the severe deformation, rapid movement, and complex background interference of objects often lead to low accuracy in underwater object tracking. To address the above challenges, we propose an underwater object tracking method based on siamese networks. The proposed parallel attention module facilitates the aggregation of similar semantic features from different positions and promotes information exchange between the two branches, enhancing the feature expression capability between channels in each branch. Moreover, the multi-scale feature fusion module effectively integrates features from various levels to adapt to changes in the target’s appearance. Finally, comprehensive experiments were conducted on the OTB100, VOT2018, and underwater dataset UT40, demonstrating the method has good performance in underwater object tracking.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant 62073120, and Natural Science Foundation of Jiangsu Province under Grant BK20201311.

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, 211100, China

    Jinbiao Sun, Huibin Wang, Zhe Chen & Lili Zhang

Authors
  1. Jinbiao Sun
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  2. Huibin Wang
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  3. Zhe Chen
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  4. Lili Zhang
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Corresponding author

Correspondence to Huibin Wang .

Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Fukuoka, Japan

    Huimin Lu

  2. Southeast University, Nanjing, China

    Jintong Cai

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sun, J., Wang, H., Chen, Z., Zhang, L. (2024). Parallel Attention Mechanism Based Multi-feature Fusion for Underwater Object Tracking. In: Lu, H., Cai, J. (eds) Artificial Intelligence and Robotics. ISAIR 2023. Communications in Computer and Information Science, vol 1998. Springer, Singapore. https://doi.org/10.1007/978-981-99-9109-9_33

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  • DOI: https://doi.org/10.1007/978-981-99-9109-9_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9108-2

  • Online ISBN: 978-981-99-9109-9

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