• Laser & Optoelectronics Progress
  • Vol. 59, Issue 12, 1215004 (2022)
Guancheng Hui1, Kaifang Li1, Ming Xin3, and Miaohui Zhang1、2、*
Author Affiliations
  • 1School of Artificial Intelligence, Henan University, Kaifeng 475004, Henan , China
  • 2Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, Henan , China
  • 3School of Computer Science and Engineering, Beihang University, Beijing 100191, China
  • show less
    DOI: 10.3788/LOP202259.1215004 Cite this Article Set citation alerts
    Guancheng Hui, Kaifang Li, Ming Xin, Miaohui Zhang. Tracking Algorithm Based on Video Person Reidentification and Spatiotemporal Feature Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215004 Copy Citation Text show less
    Joint network architecture
    Fig. 1. Joint network architecture
    Encoder-decoder network
    Fig. 2. Encoder-decoder network
    Detection branch outputing the heat map, center offset, and box size to determine the information of the bounding box and the re-identify branch outputing the classification probability of each ID
    Fig. 3. Detection branch outputing the heat map, center offset, and box size to determine the information of the bounding box and the re-identify branch outputing the classification probability of each ID
    Tracking result comparison between the MSC network and the original ResNet-34 network. (a) Detection result of the original ResNet-34 network; (b) detection result of the MSC network; (c) structure diagram of the original ResNet-34 network; (d) structure diagram of the MSC network
    Fig. 4. Tracking result comparison between the MSC network and the original ResNet-34 network. (a) Detection result of the original ResNet-34 network; (b) detection result of the MSC network; (c) structure diagram of the original ResNet-34 network; (d) structure diagram of the MSC network
    Candidate box selection based on unified scoring mechanism
    Fig. 5. Candidate box selection based on unified scoring mechanism
    Output results of the proposed method on MOT17 test set
    Fig. 6. Output results of the proposed method on MOT17 test set
    Reasoning time of MSC network and ResNet-34 network on three data sets
    Fig. 7. Reasoning time of MSC network and ResNet-34 network on three data sets
    ConfigurationParameter
    Operating systemUbuntu 16.04
    RAM(random processing unit)128 G
    CPU(central processing unit)2.50 GHz E5-2678 v3
    GPU(graphics processing unit)Tesla T4 16 G
    Software platformPytorch 1.1 Python 3.6
    Table 1. Experimental platform parameters
    DimensionMOTAIDF1IDsTime /s
    51268.573.731224.1
    25668.572.833726.1
    12869.172.529926.6
    6469.272.328326.8
    Table 2. Recognition feature dimensions evaluated on the MOT17 validation set
    Box IoUre-ID FeaturesKalman FilterMOTAIDF1IDs
    67.867.2648
    68.170.3435
    68.971.8342
    69.172.8299
    Table 3. Evaluation of the three elements associated with the evaluation data on the MOT17 validation set
    NetworkMOTAIDF1IDs
    ResNet-3463.667.2435
    MSC69.172.8299
    Table 4. Comparison between MSC network and ResNet-34 network on MOT17 validation set
    DatasetNumber of imagesNumber of boxesNumber of identitiesMOTAIDF1IDs
    MOT175×103112×1030.5×10369.172.8299
    MIX54×103270×1038.7×10373.780.1209
    Table 5. Result using different datasets for training
    DatasetTrackerMOTAIDF1MT /%ML /%IDsTime /s
    MOT16EAMTT2352.553.319.934.9910<5.5
    SORTwHPD162459.853.825.422.71423<8.6
    DeepSORT_22561.462.232.818.2781<6.4
    RAR16wVGG2663.063.839.922.1482<1.4
    VMaxx2762.649.232.721.11389<3.9
    TubeTK2864.059.433.519.411171.0
    JDE364.455.835.420.0154418.5
    TAP2964.873.538.521.6571<8.0
    CNNMTT3065.262.232.421.3946<5.3
    POI3166.165.134.020.8805<5.0
    CTackerVI3267.657.232.923.118976.8
    Proposed method74.780.238.1021.4721013.3
    MOT17SST3352.449.521.430.78431<3.96
    TubeTK2863.058.631.219.941373.0
    CTackerVI3266.657.432.224.255296.8
    CenterTack3467.359.934.624.6289822.0
    Proposed method73.780.136.9922.8920912.70
    MOT20ArTIST-T3553.651.031.628.11531
    MPNTrack3657.659.138.222.51210
    Proposed method66.472.846.8714.84140312.0
    Table 6. Comparison of results of different methods
    Guancheng Hui, Kaifang Li, Ming Xin, Miaohui Zhang. Tracking Algorithm Based on Video Person Reidentification and Spatiotemporal Feature Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215004
    Download Citation