Traffic data collection is crucial for the management of road networks. Based on YOLOv5s and DeepSORT, optimizations are made in the vehicle detection stage and the vehicle tracking stage to improve the accuracy of the system. At the same time, Support Vector Machine(SVM) is introduced to divide the upper and lower lanes, and the reference lines are automatically defined for videos taken from different perspectives. This solves the shortcomings of traditional vehicle counting methods that need to use manual annotation for each video to distinguish upper and lower lanes and set reference lines. Finally, the experimental results show that the system is able to achieve an average accuracy of 95.5% in real time in daytime, evening, foggy, and sunny conditions.


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    Titel :

    A Vehicle Counting and Road Condition Analysis System Based on Multiple Object Tracking


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Yingmin (Herausgeber:in) / Zhang, Weicun (Herausgeber:in) / Fu, Yongling (Herausgeber:in) / Zhao, Shoujun (Herausgeber:in) / Min, Yusong (Autor:in) / Zhai, Junyong (Autor:in)

    Kongress:

    Chinese Intelligent Systems Conference ; 2022 October 15, 2022 - October 16, 2022



    Erscheinungsdatum :

    27.09.2022


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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