Infrared and Laser Engineering, Volume. 51, Issue 2, 20210864(2022)

Target tracking acceleration scheme adopting adaptive fuzzy optimization

Cong Xu... Daying Sun, Ziqi Cao, Chunqi Li and Wenhua Gu |Show fewer author(s)
Author Affiliations
  • School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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    Figures & Tables(11)
    Flowchart of target tracking acceleration algorithm based on kernel correlation filtering
    Qualitative results analysis of origin algorithm and optimized algorithm
    Combined results of system clustering
    Diagram of sequence for quantization classification
    Tracking response results of Lemming sequence sets under different conditions
    A re-search of the tracking target
    Module diagram of hardware acceleration IP
    Performance comparison for adaptive fuzzy optimization
    Comparison of tracking accuracy between different tracking algorithms
    • Table 1. Tracking speed comparison of different tracking algorithms

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      Table 1. Tracking speed comparison of different tracking algorithms

      TrackerProposedStructTLDCNDSSTKCF
      Tracking speed/frame·s−1316.817.424.49019.3254.8
    • Table 2. Comparison of tracking results based on PYNQ

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      Table 2. Comparison of tracking results based on PYNQ

      PlatformTracking precisionTracking speed/ frame·s−1
      Based on ARM60.3%2.15
      Based on ARM and FPGA65.8%17.28
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    Cong Xu, Daying Sun, Ziqi Cao, Chunqi Li, Wenhua Gu. Target tracking acceleration scheme adopting adaptive fuzzy optimization[J]. Infrared and Laser Engineering, 2022, 51(2): 20210864

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    Paper Information

    Category: Special issue-Infrared low light level night vision technology

    Received: Nov. 19, 2021

    Accepted: --

    Published Online: Mar. 21, 2022

    The Author Email:

    DOI:10.3788/IRLA20210864

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