Volume 41 Issue 3
Jun.  2023
Turn off MathJax
Article Contents
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.

     

    • FullText(HTML)
  • loading
    • References(28)
  • [1]
    吕伟, 黄广琛, 汪京辉. 基于元胞自动机的高速公路瓶颈交通演化仿真[J]. 交通运输系统工程与信息, 2022, 22(3): 293-302. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202203033.htm

    LYU W, HUANG G S, WANG J H. Simulation of highway traffic bottleneck via cellular automata[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 293-302. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202203033.htm
    [2]
    SUN D H., CHEN D, ZHAO M., et al. Linear stability and nonlinear analyses of traffic waves for the general nonlinear car-following model with multi-time delays[J]. Physica A: Statistical Mechanics and its Applications. 2018, 501: 293-307. doi: 10.1016/j.physa.2018.02.179
    [3]
    朱顺应, 蒋若曦, 王红, 等. 机动车交通冲突技术研究综述[J]. 中国公路学报, 2020, 33(2): 15-33. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202002002.htm

    ZHU S Y, JIANG R X, WANG H, et al. Review of research on traffic conflict techniques[J]. China Journal of Highway and Transport, 2020, 33(2): 15-33. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202002002.htm
    [4]
    WU K, JOVANIS P. Crashes and crash-surrogate events: Exploratory modeling with naturalistic driving data[J]. Accident Analysis & Prevention, 2012(45): 507-516.
    [5]
    BAGDADI O. Assessing safety critical braking events in naturalistic driving studies[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2013(16): 117-126.
    [6]
    PARSA A B, TAGHIPOUR H, DERRIBLE S, et al. Real-time accident detection: Coping with imbalanced data[J]. Accident Analysis & Prevention, 2019(129): 202-210.
    [7]
    吕能超, 彭凌枫, 吴超仲, 等. 区分冲突类型的路段实时碰撞风险预测模型[J]. 中国公路学报, 2022, 35(1): 93-108.

    LYU N C, PENG L F, WU C Z, et al. Real-time crash-risk prediction model that distinguishes collision types[J]. China Journal of Highway and Transport, 2022, 35(1): 93-108. (in Chinese)
    [8]
    关丽敏, 张倩, 楚庆玲, 等. 基于改进ICP算法的路侧双激光雷达数据融合[J]. 激光杂志, 2021, 42(9): 38-44. https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ202109008.htm

    GUAN L M, ZHANG Q, CHU Q L, et al. Roadside dual lidar data fusion based on improved ICP algorithm[J]. Laser Journal, 2021, 42(9): 38-44. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JGZZ202109008.htm
    [9]
    薛清文, 蒋愚明, 陆键. 基于轨迹数据的危险驾驶行为识别方法[J]. 中国公路学报, 2020, 33(6): 84-94. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202006009.htm

    XUE Q W, JIANG Y M, LU J. Risky driving behavior recognition based on trajectory data[J]. China Journal of Highway and Transport, 2020, 33(6): 84-94. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202006009.htm
    [10]
    王俊骅, 宋昊, 景强, 等. 基于毫米波雷达组群的全域车辆轨迹检测技术方法[J]. 中国公路学报, 2022, 35(12): 181-192. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202212015.htm

    WANG J H, SONG H, JING Q, et al. Road-range tracking of vehicle trajectories based on millimeter-wave radar[J]. China Journal of Highway and Transport, 2022, 35(12): 181-192. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202212015.htm
    [11]
    李熙莹, 梁靖茹, 郝腾龙. 考虑连锁冲突的城市公交车行车风险量化分析方法[J]. 交通信息与安全, 2022, 40(3): 19-29. doi: 10.3963/j.jssn.1674-4861.2022.03.003

    LI X Y, LIANG J R, HAO T L. A method for quantitatively analyzing risks associated with the operation of urban buses considering chained conflicts[J]. Journal of Transport Information and Safety. 2022, 40(3): 19-29. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.03.003
    [12]
    WANG C, XU C C, DAI Y L. A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data[J]. Accident Analysis & Prevention, 2019(123): 365-373.
    [13]
    房锐, 张琪, 胡澄宇, 等. 基于风险矩阵的干线公路弯道路段交通冲突风险评估模型[J]. 交通运输系统工程与信息, 2021, 21(2): 166-172. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202102026.htm

    FANG R, ZHANG Q, HU C G, et al. Risk assessment model based on risk matrix for traffic conflict on arterial highway bend section[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 166-172. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202102026.htm
    [14]
    KRAJEWSKI R, BOCK J, KLOEKER L, et al. The HighD dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems[C]. 21st International Conference on Intelligent Transportation Systems(ITSC), Maui, HI, USA: IEEE, 2018.
    [15]
    冯汝怡, 李志斌, 吴启范, 等. 航拍视频车辆检测目标关联与时空轨迹匹配[J]. 交通信息与安全, 2021, 39(2): 61-69, 77. doi: 10.3963/j.jssn.1674-4861.2021.02.008

    FENG R Y, LI Z B, WU Q F, et al. Association of vehicle object detection and the time-space trajectory matching from aerial videos[J]. Journal of Transport Information and Safety, 2021, 39(2): 61-69, 77. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.02.008
    [16]
    FHWA, Department of Transportation, America. NGSIM: Next generation simulation[EB/OL]. (2007-5-5)[2015-08-16]. http://www.ngsim-community.org/.
    [17]
    李枫. 高速公路视频监控系统的应用及构建[J]. 电子技术与软件工程, 2023, 243(1): 159-164. https://www.cnki.com.cn/Article/CJFDTOTAL-DZRU202301033.htm

    LI F. Application and construction of expressway video monitoring system[J]. Electronic Technology and Software Engineering, 2023, 243(1): 159-164. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DZRU202301033.htm
    [18]
    李子腾, 施绍武, 张康. 大数据+AI收费稽核系统[J]. 中国交通信息化, 2022, 269(5): 95-98. https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202205011.htm

    LI Z T, SHI S W, ZHANG K. Big data + AI fee audit system[J]. China's Transportation Informatization, 2022, 269 (5): 95-98. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202205011.htm
    [19]
    李君羡, 童文聪, 沈宙彪, 等. 基于结构化视频数据的交叉口评估及问题自动化诊断[J]. 同济大学学报(自然科学版), 2020, 48(8): 1149-1160. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ202008008.htm

    LI J X, TONG W C, SHEN Z B, et al. Intersection evaluation and automatic problem diagnosis based on structured video data[J]. Journal of Tongji University (Natural Science), 2020, 48(8): 1149-1160. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ202008008.htm
    [20]
    REDMON J, FARHADI A. YOLO9000: Better, faster, Stronger. [C]. 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, USA: IEEE, 2017.
    [21]
    李建国, 张睿, 王凯, 等. 基于匈牙利匹配和卡尔曼滤波的动态多目标跟踪[J]. 汽车实用技术, 2022, 47(1): 45-50. https://www.cnki.com.cn/Article/CJFDTOTAL-SXQC202201011.htm

    LI J G, ZHANG R, WANG K, ET AL. Multi-object tracking algorithm based on Hungarian matching and Kalman filtering[J]. Automobile Applied Technology, 2022, 47(1): 45-50. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SXQC202201011.htm
    [22]
    李月峰, 周书仁. 在线多目标视频跟踪算法综述[J]. 计算技术与自动化, 2018, 37(1): 73-82. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJH201801016.htm

    LI Y F, ZHOU S R. Survey of online multi-object video tracking algorithms[J]. Computing Technology and Automation, 2018, 37(01): 73-82. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJH201801016.htm
    [23]
    戴雯惠, 樊凌. 基于改进透视变换的畸变图像校正方法研究[J]. 信息通信, 2020, 215(11): 63-65. https://www.cnki.com.cn/Article/CJFDTOTAL-HBYD202011021.htm

    DAI W H, FAN L. Research on distorted image correction method based on improved perspective transform[J], Information Communication, 2020, 215(11): 63-65. https://www.cnki.com.cn/Article/CJFDTOTAL-HBYD202011021.htm
    [24]
    MUTHALAGU R, BOLIMERA A, KALAICHELVI V. Lane detection technique based on perspective transformation and histogram analysis for self-driving cars[J]. Computers and Electrical Engineering, 2020, 85(1): 106653.
    [25]
    王德咏, 葛修润, 罗先启, 等. 基于改进DLT算法的数字近景摄影测量[J]. 上海交通大学学报, 2011, 45(增刊1): 16-20, 26. https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT2011S1005.htm

    WANG D Y, GE X R, LUO X Q, et al. Study on digital close-range photogrammetry based on improved DLT algorithm[J]. Journal of Shanghai Jiao Tong University 2011, 45 (S1): 16-20, 26. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT2011S1005.htm
    [26]
    YUN S, HAN D, CHUN S, et al. CutMix: regularization strategy to train strong classifiers with localizable features[C]. IEEE/CVF International Conference on Computer Vision(ICCV), Seoul, South Korea: IEEE, 2019.
    [27]
    WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]. The European Conference on Computer Vision(ECCV)Munich, Germany: IEEE, 2018.
    [28]
    彭丁聪. 卡尔曼滤波的基本原理及应用[J]. 软件导刊, 2009, 8(11): 32-34. https://www.cnki.com.cn/Article/CJFDTOTAL-RJDK200911011.htm

    PENG D C. Basic principle and application of kalman filter[J]. Software Guide, 2009, 8(11): 32-34. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-RJDK200911011.htm
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(15)  / Tables(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (368) PDF downloads(23) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return