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Temporal Global Re-detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14356))

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Abstract

In long-term visual tracking, target occlusion and out-of-view are common problems that lead to target drift, adding re-detection to short-term tracking algorithms is a general solution. To better handle the problem of target disappearing and reappearing in long-term visual tracking, this paper proposes a temporal global re-detection method based on interaction-fusion attention. Firstly, ResNet50 is used as the feature extraction network to obtain the depth features of the template and the search region. Then, a new interaction-fusion attention is added to extract the connection of different dimensionality of features. Finally, Temporal ROI Align is introduced to select candidate boxes, increasing the use of historical information by re-detection method, and improving the accuracy of target localization. STMTrack algorithm is selected as the short-term tracking algorithm, which works with the proposed re-detection method to construct a long-term tracking algorithm, and experiments are conducted on the UAV123, LaSOT, UAV20L, and VOT2018-LT datasets, and the effectiveness of this re-detection method can be seen from the experimental results.

Supported by the National Natural Science Foundation of China under grant no. 62072370 and the Natural Science Foundation of Shaanxi Province under grant no. 2023-JC-YB-598.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under grant no. 62072370 and the Natural Science Foundation of Shaanxi Province under grant no. 2023-JC-YB-598.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Jingyuan Ma, Zhiqiang Hou, Ruoxue Han & Sugang Ma

  2. Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province, Xi’an, 710121, China

    Jingyuan Ma, Zhiqiang Hou, Ruoxue Han & Sugang Ma

Authors
  1. Jingyuan Ma
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  2. Zhiqiang Hou
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  3. Ruoxue Han
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  4. Sugang Ma
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Corresponding author

Correspondence to Jingyuan Ma .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. University of Sydney, Sydney, NSW, Australia

    Wanli Ouyang

  3. Shenzhen University, Shenzhen, China

    Hui Huang

  4. Tsinghua University, Beijing, China

    Jiwen Lu

  5. Dalian University of Technology, Dalian, China

    Risheng Liu

  6. Institute of Automation, CAS, Beijing, China

    Jing Dong

  7. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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Ma, J., Hou, Z., Han, R., Ma, S. (2023). Temporal Global Re-detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14356. Springer, Cham. https://doi.org/10.1007/978-3-031-46308-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-46308-2_1

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-46308-2

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