• Journal of Applied Optics
  • Vol. 44, Issue 4, 768 (2023)
Nianfu ZHAO1,2, Lin WANG1,2, Xiangjun WANG1,2, and Wenliang CHEN1,2,*
Author Affiliations
  • 1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
  • 2MOEMS Education Ministry Key Laboratory, Tianjin University, Tianjin 300072, China
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    DOI: 10.5768/JAO202344.0402001 Cite this Article
    Nianfu ZHAO, Lin WANG, Xiangjun WANG, Wenliang CHEN. Re-detection method for long-term tracking based on improved two-stage detection networks[J]. Journal of Applied Optics, 2023, 44(4): 768 Copy Citation Text show less
    Framework of algorithm
    Fig. 1. Framework of algorithm
    Template region matching method of typical deep cross-correlation
    Fig. 2. Template region matching method of typical deep cross-correlation
    Network structure of enhancement-and-correlation stage
    Fig. 3. Network structure of enhancement-and-correlation stage
    Comparison of re-detection effects
    Fig. 4. Comparison of re-detection effects
    Dynamic interaction module used in proposed method
    Fig. 5. Dynamic interaction module used in proposed method
    Comparison of experimental results on LaSOT dataset
    Fig. 6. Comparison of experimental results on LaSOT dataset
    Experimental results under subdivision scene of LaSOT dataset
    Fig. 7. Experimental results under subdivision scene of LaSOT dataset
    跟踪器MaxGMTPRTNR
    LTMU[14]0.7510.7490.754
    本文方法0.6230.6290.674
    SPLT[23]0.6220.4980.776
    GlobalTrack(原始方法)[13]0.6030.5740.633
    SiamFC+R[24]0.4540.4270.481
    Table 1. Experimental results on OxUva dataset
    优化方法精确率成功率帧率/(帧/s)
    原始方法0.5170.5286.0
    动态交互模块0.5260.53919.2
    动态交互模块+交叉增强模块0.5280.55816.8
    动态交互模块+模板信息引导0.5430.55118.9
    本文方法0.5450.56116.4
    Table 2. Results of ablation experiments
    Nianfu ZHAO, Lin WANG, Xiangjun WANG, Wenliang CHEN. Re-detection method for long-term tracking based on improved two-stage detection networks[J]. Journal of Applied Optics, 2023, 44(4): 768
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