Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 8, 1128(2023)

Stable and real-time pedestrian tracking method based on improved DeepSORT under complex background

Li-juan ZHANG1,2, Zi-wei ZHANG2, Yu-tong JIANG3、*, Dong-ming LI1,4, Meng-da HU2, and Ying-xue LIU2
Author Affiliations
  • 1School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China
  • 2College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
  • 3China North Vehicle Research Institute, Beijing 100072, China
  • 4School of Information Technology, Jilin Agricultural University, Changchun 130118, China
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    Figures & Tables(13)
    Pedestrian multi-objective algorithm framework proposed in this paper
    YOLOv5 framework
    Coordinate attention structure diagram
    Improved YOLOv5 backbone network
    Successfully matched tracks
    Cascade matching in DeepSORT
    Ablation experiment of detection algorithm
    FPS and GPU cost
    Visualization of tracking effect of improved algorithm in MOT16-02
    • Table 1. Evaluation index of multi-target tracking

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      Table 1. Evaluation index of multi-target tracking

      指标含义
      MOTA跟踪精度,衡量目标轨迹的保持程度
      HOTA

      更高维跟踪精度,更加重视目标检测和

      数据关联精度的平均性测量

      IDF1衡量跟踪器身份维持能力
      MT

      整个视频中超过80%的时间被正确

      跟踪的轨迹个数

      ML

      整个视频中不超过20%时间被正确

      跟踪的轨迹个数

      IDSW目标身份的切换次数
      Frages轨迹碎片的个数
    • Table 2. Ablation experiment results of three models on MOT16 dataset

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      Table 2. Ablation experiment results of three models on MOT16 dataset

      模型HOTA↑/%MOTA↑/%IDF1↑/%IDSW↓MT↑/%ML↓/%Frages↓
      YOLOv5+DeepSORT43.353.551.01 05129.626.51 173
      YOLOv5+CoorATT+DeepSORT51.260.657.190632.924.4948
      YOLOv5+CoorATT+DeepSORT+KCF57.166.564.264135.021.7620
    • Table 3. Comparison results between the algorithm in this paper and other advanced algorithms on MOT16 dataset

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      Table 3. Comparison results between the algorithm in this paper and other advanced algorithms on MOT16 dataset

      HOTA↑/%MOTA↑/%IDF1↑/%IDSW↓MT↑/%ML↓/%FPS↑
      Tracktor++1347.356.355.11 24719.536.615
      CenterTrack2048.860.757.21 30932.923.113.8
      DeepMOT2142.453.753.81 64719.137.04.9
      TubeTK2248.763.258.61 13733.519.41.0
      FairMOT2355.365.366.787434.720.923.7
      本文方法57.166.564.264135.021.722.6
    • Table 4. Comparison results between the algorithm in this paper and other advanced algorithms on MOT17 dataset

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      Table 4. Comparison results between the algorithm in this paper and other advanced algorithms on MOT17 dataset

      HOTA↑/%MOTA↑/%IDF1↑/%IDSW↓MT↑/%ML↓/%FPS↑
      Tracktor++1351.953.552.32 70617.935.214
      CenterTrack2050.261.557.42 89827.424.817
      DeepMOT1846.454.255.73 94718.637.96.9
      TubeTK2248.062.058.64 13729.122.93.0
      FairMOT2357.467.362.53 30333.220.322.9
      本文方法58.365.762.82 07233.819.924
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    Li-juan ZHANG, Zi-wei ZHANG, Yu-tong JIANG, Dong-ming LI, Meng-da HU, Ying-xue LIU. Stable and real-time pedestrian tracking method based on improved DeepSORT under complex background[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(8): 1128

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    Paper Information

    Category: Research Articles

    Received: Oct. 22, 2022

    Accepted: --

    Published Online: Oct. 9, 2023

    The Author Email: Yu-tong JIANG (jiangyutong201@163.com)

    DOI:10.37188/CJLCD.2022-0350

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