• Infrared and Laser Engineering
  • Vol. 50, Issue 9, 20200491 (2021)
Hongwei Zhang1, Xiaoxia Li1、2, Bin Zhu1, and Yang Zhang1
Author Affiliations
  • 1School of Electronic Countermeasure, National University of Defense Technology, Hefei 230037, China
  • 2State Key Laboratory of Pulsed Power Laser Technology, Hefei 230037, China
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    DOI: 10.3788/IRLA20200491 Cite this Article
    Hongwei Zhang, Xiaoxia Li, Bin Zhu, Yang Zhang. Two-stage object tracking method based on Siamese neural network[J]. Infrared and Laser Engineering, 2021, 50(9): 20200491 Copy Citation Text show less
    Classification response map of SiamRPN
    Fig. 1. Classification response map of SiamRPN
    Schematic diagram of the two-stage tracking method based on Siamese network
    Fig. 2. Schematic diagram of the two-stage tracking method based on Siamese network
    Features extracted by different networks
    Fig. 3. Features extracted by different networks
    Comparative analysis of network outputs after applying correlation filter modulation
    Fig. 4. Comparative analysis of network outputs after applying correlation filter modulation
    Schematic diagram of the verification network
    Fig. 5. Schematic diagram of the verification network
    Comparison of classification distances of the inter-object and intra-object
    Fig. 6. Comparison of classification distances of the inter-object and intra-object
    Comparison of classification distance between different sampling groups
    Fig. 7. Comparison of classification distance between different sampling groups
    Evaluation on the OTB100 benchmark: Precision plot and success plot
    Fig. 8. Evaluation on the OTB100 benchmark: Precision plot and success plot
    AR scores in baseline test condition
    Fig. 9. AR scores in baseline test condition
    Overlap rate curves in unsupervised condition
    Fig. 10. Overlap rate curves in unsupervised condition
    Test results on the UAV123 dataset: Precision plot and success plot
    Fig. 11. Test results on the UAV123 dataset: Precision plot and success plot
    Qualitative evaluation of challenging sequences
    Fig. 12. Qualitative evaluation of challenging sequences
    Ability to deal with the similar interference of the two-stage Siamese network
    Fig. 13. Ability to deal with the similar interference of the two-stage Siamese network
    Comparison of regression precision of the two-stage Siamese network
    Fig. 14. Comparison of regression precision of the two-stage Siamese network
    Running time test of each module
    Fig. 15. Running time test of each module
    BaselineUnsupervised
    A-R rankEAOOverlapSpeed
    OverlapFailuresEAOAUCNormalizedFPS
    Ours0.601 114.515 90.383 30.533 93.496 120.245 1
    LADCF0.491 19.925 30.38110.418 20.123 00.557 3
    MFT0.491 910.766 20.379 40.391 70.194 50.623 2
    DaSiamRPN0.569 118.441 50.378 50.468 417.818 364.414 3
    UPDT0.515 411.417 20.371 90.444 40.088 40.469 7
    RCO0.498 910.700 40.371 10.383 00.204 60.720 3
    SiamRPN0.591 519.632 50.369 10.456 820.342 686.784 3
    DRT0.495 813.947 60.349 00.419 10.123 70.456 8
    DeepSTRCF0.506 214.548 60.338 30.433 30.560 53.114 4
    CPT0.488 816.620 70.332 10.375 70.877 15.184 2
    SA_Siam_R0.544 416.403 00.331 10.425 06.776 132.364 4
    DLSTpp0.529 714.937 40.321 30.497 81.293 08.175 9
    Table 1. Evaluation results on the VOT benchmark
    Hongwei Zhang, Xiaoxia Li, Bin Zhu, Yang Zhang. Two-stage object tracking method based on Siamese neural network[J]. Infrared and Laser Engineering, 2021, 50(9): 20200491
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