Volume 41 Issue 3
Jun.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(3): 80-91
LIU Chao, LUO Ruyi, LIU Chunqing, LYU Nengchao. A Method for Developing Continuous Vehicle Trajectories through Target Association and Trajectory Splicing Based on Video Data from Multiple Roadside Cameras[J]. Journal of Transport Information and Safety, 2023, 41(3): 80-91. doi: 10.3963/j.jssn.1674-4861.2023.03.009
Citation: LIU Chao, LUO Ruyi, LIU Chunqing, LYU Nengchao. A Method for Developing Continuous Vehicle Trajectories through Target Association and Trajectory Splicing Based on Video Data from Multiple Roadside Cameras[J]. Journal of Transport Information and Safety, 2023, 41(3): 80-91. doi: 10.3963/j.jssn.1674-4861.2023.03.009
shu

A Method for Developing Continuous Vehicle Trajectories through Target Association and Trajectory Splicing Based on Video Data from Multiple Roadside Cameras

doi: 10.3963/j.jssn.1674-4861.2023.03.009
  • 1.

    Intelligent Transportation System Research Center, Wuhan University of Technology, Hubei Wuhan 430063, China

  • 2.

    School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

  • 3.

    Hubei Communications Investment Technology Development Co., Ltd Wuhan 430034

  • Received Date: 2022-10-25
    Available Online: 2023-09-16
    Abstract
  • A method for developing continuous vehicle trajectories through multiple roadside cameras is proposed to address the limited coverage of a single camera. This study sets up multiple fixed cameras on the roadside to col-lect video data, and solves the problem of image distortion caused by camera extrinsic parameters through direct linear transformation algorithm. Training samples are evenly extracted from images of all time periods and road areas, and a vehicle detection model is trained using convolutional neural network YOLOv5. For the occasional missed vehicles, an integrity check method can be used to screen missing vehicles and get the problem fixed. In cases where a vehicle is missed or falsely detected in multiple consecutive frames, the target association problem is solved through the use of checking algorithm for abnormal trajectory and data repair plugin. A repair algorithm is proposed to solve the problem of deformation of vehicle profile in the areas diagonally below the camera, which solves the problem of varying detection box sizes for the same vehicles traveling at different road segments. And a method for vehicle trajectory splicing between adjacent cameras is proposed based on the centroid coordinates of vehicle. The development of continuous vehicle trajectory dataset among continuous multiple cameras is achieved under the premise of time synchronization among multiple cameras. By using the methods of target association and trajectory splicing mentioned above, a continuous vehicle trajectory dataset covering Luoshi Road Overpass in Wuhan has been developed using a time synchronization method for different locations. Study results of track data set show that: the dataset covers various traffic flow states from free-flow to congested, including multiple diver-sion and merging areas. The dataset has a continuous duration of 3.5 hours and covers an area of 1.41 km. Study results of the vehicle detection model show that the recall rate of the model is 93.23%, the precision rate is 98.51%, and the F1 score is 95.80%. According to the data self-inspection results, the dataset contains a total of 25 734 trajectories from the arterial roads and ramps, including 15 004 trajectories covering the entire road. The method proposed in this study provides a technical framework for target association and trajectory splicing of video data from multiple roadside cameras, and a way of developing continuous vehicle trajectories for better traffic man-agement and control.

     

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