Optics and Precision Engineering, Volume. 33, Issue 2, 324(2025)

Special attribute-based cross-modal interactive fusion network for RGBT tracking

Xiaoqiang SHAO, Hao LI*, Zhiyue LÜ, Bo MA, Mingqian LIU, and Zehui HAN
Author Affiliations
  • College of Electrical and Control Engineering, Xi’an University of Science and Technology,Xi'an710054, China
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    Figures & Tables(15)
    Proposed network framework
    Special Challenge Attribute Fusion Module
    Common Challenge Attribute Fusion Module
    Overall structure of the cross-modal interaction module
    Result of comparing our loss with binary loss
    Feature extraction results from the first stage
    Results of the evaluation on the GTOT dataset
    Evaluation results on the RGBT234 dataset
    Results of the evaluation on the Lasher dataset
    Visualization of tracking results for different frames under different video sequences in the Lasher datase
    • Table 1. Performance evaluation of our tracker in GTOT, RGBT234, Lasher datasets

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      Table 1. Performance evaluation of our tracker in GTOT, RGBT234, Lasher datasets

      数据集PRSR
      GTOT91.7%73.9%
      RGBT23484.1%57.3%
      Lasher52.3%39.1%
    • Table 2. PR/SR of the proposed tracker for different attributes of the RGBT234 dataset

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      Table 2. PR/SR of the proposed tracker for different attributes of the RGBT234 dataset

      NumMANetMDNetDAPNetCATADRNetAPFNetOur
      NO4191.4/64.981.2/59.090.4/64.493.2/66.889.8/64.893.4/66.493.7/66.3
      PO9683.1/58.474.7/50.982.1/57.485.1/59.382.7/58.285.0/58.788.8/63.4
      HO9666.5/45.963.3/43.266.0/45.770.0/48.070.9/49.472.9/49.072.5/48.2
      LI6381.7/56.058.9/39.677.5/53.081.0/54.779.8/54.582.3/54.487.3/60.6
      LR5078.5/51.366.0/44.575.0/51.082.0/53.980.2/54.282.9/54.886.7/60.8
      TC2872.9/52.974.8/53.076.8/54.380.3/57.779.7/57.882.1/57.382.9/57.5
      DEF7672.3/52.466.4/46.871.7/51.876.2/54.172.1/51.277.1/54.676.5/53.6
      FM3271.1/45.563.2/39.367.0/44.373.1/47.073.9/48.878.2/49.278.6/49.3
      SC12077.8/54.873.9/51.978.0/54.279.7/56.678.5/56.182.1/56.582.4/56.7
      MB5566.8/48.262.4/44.265.3/46.768.3/49.070.6/50.672.8/53.071.5/52.6
      CM8969.9/50.561.3/43.366.8/47.475.2/52.773.9/52.376.3/54.583.3/59.0
      BC5475.6/49.862.5/41.871.7/48.481.1/51.974.3/50.280.6/52.480.4/51.3
      FPS-1.11220251.93.5
      ALL23477.8/54.471.0/49.076.6/53.780.4/56.179.2/55.882.7/57.984.1/57.3
    • Table 3. Results of challenge attribute in GTOT dataset

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      Table 3. Results of challenge attribute in GTOT dataset

      数据集

      ILL-

      Branch

      TC-

      Branch

      CONNON-

      Branch

      ILL43.4%37.3%39.7%
      TC88.5%89.2%87.2%
      COMMON72.8%72.4%74.9%
    • Table 4. Results of challenge attribute in RGBT234 dataset

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      Table 4. Results of challenge attribute in RGBT234 dataset

      数据集

      ILL-

      Branch

      TC-

      Branch

      CONNON-

      Branch

      ILL65.3%57.3%59.4%
      TC68.9%75.3%67.6%
      COMMON62.3%68.4%70.3%
    • Table 5. Ablation experiments

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      Table 5. Ablation experiments

      数据集CSNCMIFPSPRSR
      GTOT4.290.5%72.5%
      3.891.1%73.4%
      3.591.7%73.9%
      RGBT2344.281.3%56.4%
      3.882.6%56.8%
      3.584.1%57.3%
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    Xiaoqiang SHAO, Hao LI, Zhiyue LÜ, Bo MA, Mingqian LIU, Zehui HAN. Special attribute-based cross-modal interactive fusion network for RGBT tracking[J]. Optics and Precision Engineering, 2025, 33(2): 324

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    Paper Information

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    Received: Jul. 9, 2024

    Accepted: --

    Published Online: Apr. 30, 2025

    The Author Email: Hao LI (2670815399@qq.com)

    DOI:10.37188/OPE.20253302.0324

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