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Laser & Optoelectronics Progress, Volume. 57, Issue 18, 181025(2020)

Residual Network Feature Fusion Tracking Algorithm Based on Graph Salience Detection

Hui Jin1,2 and Xinyang Li1、*
Author Affiliations
  • 1Key Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
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    References (19)
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    Figures & Tables(9)
    Steps of target location

    Fig. 1. Steps of target location

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    Original image and GBVS image. (a) Original image; (b) GBVS image

    Fig. 2. Original image and GBVS image. (a) Original image; (b) GBVS image

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    Improved flow chart based on GBVS

    Fig. 3. Improved flow chart based on GBVS

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    Average center error of the improved algorithm based on GBVS

    Fig. 4. Average center error of the improved algorithm based on GBVS

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    Tracking effect of the algorithm. (a) Before improvement; (b) after improvement

    Fig. 5. Tracking effect of the algorithm. (a) Before improvement; (b) after improvement

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    Tracking accuracy of different algorithms on the OTB50 data set. (a) Total accuracy; (b) fast movement; (c) motion blur; (d) low resolution; (e) occlusion; (f) different scales

    Fig. 6. Tracking accuracy of different algorithms on the OTB50 data set. (a) Total accuracy; (b) fast movement; (c) motion blur; (d) low resolution; (e) occlusion; (f) different scales

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    Tracking results of different algorithms in the OTB dataset

    Fig. 7. Tracking results of different algorithms in the OTB dataset

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    • Table 1. Comparison of average center error based on GBVS improved algorithm

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      Table 1. Comparison of average center error based on GBVS improved algorithm

      Layer ofResNet-50 (number)Conv1-1(2)Addition2-1(6)Conv2-3(12)Addition2-2(26)Conv2-10(34)Addition3-1(48)
      After GBVS16.918.2812.189.304.917.43
      Before GBVS18.1510.2214.3910.786.689.55
      Error1.241.942.211.481.772.12

    • Table 2. Average central error of different algorithms

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      Table 2. Average central error of different algorithms

      AlgorithmSTRCFStapleCSKBeforeGBVSAfterGBVS
      Center error6.7955.1428.1886.6864.916
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    Hui Jin, Xinyang Li. Residual Network Feature Fusion Tracking Algorithm Based on Graph Salience Detection[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181025

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    Paper Information

    Category: Image Processing

    Received: Feb. 5, 2020

    Accepted: Mar. 25, 2020

    Published Online: Sep. 2, 2020

    The Author Email: Li Xinyang (xyli@ioe.ac.cn)

    DOI:10.3788/LOP57.181025

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology