Laser & Optoelectronics Progress, Volume. 59, Issue 8, 0815006(2022)

Automatic Extraction Method for Gait Parameters of Quadruped Walking Based on Computer Vision

Yao Chen1, Yunwei Zhang1,2,3、*, Jinhui Lei1,3, and li Li1
Author Affiliations
  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming , Yunnan 650500, China
  • 2Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming , Yunnan 650500, China
  • 3Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming , Yunnan 650500, China
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    Figures & Tables(27)
    Video acquisition system
    Images of quadruped walking
    Flow chart of automatic identification of motion feature
    Structure of DeeplabV3+
    Multiscale feature fusion module
    DUpsampling
    Improved DeeplabV3+
    Comparison of effects of semantic segmentation.(a) Original image; (b) PSPNet; (c) Segnet; (d) Unet; (e) DeeplabV3+; (f) improved DeeplabV3+Xception
    Flow chart of motion corner detection and matching
    Schematic diagram of corner swing angle
    Local maximum values
    Corner detection of quadrupeds. (a) Corners of rhino without overlap; (b) forelimb corners of rhino with overlap; (c) hindlimb corners of buffalo with overlap; (d) forelimb corners of alpaca with overlap
    Curves of corner distance variation
    Gait cycle of each limb
    Motion corner detection and matching
    Rhino’s corner distance variation curves
    Rhino’s gait cycle of each limb
    Buffalo’s corner distance variation curves
    Buffalo’s gait cycle of each limb
    Alpaca’s corner distance variation curves
    Alpaca’s gait cycle of each limb
    Gait sequences. (a) Gait sequence of buffalo; (b) gait sequence of rhino; (c) gait sequence of alpaca
    Corner swing angles. (a) Corner swing angle of rhino; (b) corner swing angle of buffalo; (c) corner swing angle of alpaca
    Corner detection for different scales. (a) R=1.06%; (b) R=3.25%; (c) R=6.24%
    • Table 1. Comparison of corner detection accuracy

      View table

      Table 1. Comparison of corner detection accuracy

      TypeDeeplabV3+Improved DeeplabV3+
      Accuracy /%Deviation value /pixelAccuracy /%Deviation value /pixel
      Rhino80.23884.632
      Buffalo82.43685.927
      Alpaca93.728100.019
    • Table 2. Values of cycle and frequency

      View table

      Table 2. Values of cycle and frequency

      TypeLimbRhinoBuffaloAlpaca
      T /sf /HzT /sf /HzT /sf /Hz

      Method of this article

      Forelimb 12.000.501.370.731.280.78
      Forelimb 22.330.431.390.721.280.78
      Hindlimb 12.100.481.380.721.280.78
      Hindlimb 21.870.531.390.721.280.78
      Average value2.080.491.380.721.280.78

      Manual calculation

      Forelimb 11.970.511.380.721.260.79
      Forelimb 22.130.471.390.721.270.79
      Hindlimb 12.070.481.360.741.300.77
      Hindlimb 21.930.521.380.721.210.82
      Average value2.070.501.380.731.260.79
      Error /%0.502.000.001.371.561.27
    • Table 3. Comparison of stride lengths of rhino, buffalo, and alpaca

      View table

      Table 3. Comparison of stride lengths of rhino, buffalo, and alpaca

      LimbRhinoBuffaloAlpaca
      Method of this articleManual calculationMethod of this articleManual calculationMethod of this articleManual calculation
      Forelimb 11.431.351.151.120.550.57
      Forelimb 21.331.221.051.100.520.50
      Hindlimb 11.401.451.101.150.500.51
      Hindlimb 21.441.381.131.170.520.54
      Average value1.401.361.111.140.520.53
      Average error /%2.852.631.89
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    Yao Chen, Yunwei Zhang, Jinhui Lei, li Li. Automatic Extraction Method for Gait Parameters of Quadruped Walking Based on Computer Vision[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0815006

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    Paper Information

    Category: Machine Vision

    Received: Mar. 8, 2021

    Accepted: Apr. 28, 2021

    Published Online: Apr. 11, 2022

    The Author Email: Zhang Yunwei (zhangyunwei72@gmail.com)

    DOI:10.3788/LOP202259.0815006

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