A robust traffic surveillance system is one of the emerging fields in the existing system; the traffic surveillance system mainly concentrates on vehicular movement for effective management of traffic. Vehicle flow detection plays a prominent role in the surveillance system. The main drawback of the existing system is it consumes more time and results in false detection due to the presence of the shadow. Hence, in this paper, mixture of Gaussian (MoG2) model is proposed to extract the foreground from the background efficiently, followed by Gaussian blur and morphological operation for efficient shadow removal. Finally, dynamic object detection is using blob detection where the vehicle is shown as a rectangular patch from which meaningful features are extracted and identified as vehicle object through blob analysis. Finally, the detected vehicles are tracked through blob tracking and counted using multiple reference lines by measuring the minimal distance between extracted features in consecutive frames. The experimental results of the proposed system show a maximum efficiency of 91%.


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    Titel :

    Vision-Based Vehicle Detection and Tracking System


    Weitere Titelangaben:

    Lecture Notes on Data Engineering and Communications Technologies


    Beteiligte:
    Sharma, Harish (Herausgeber:in) / Saraswat, Mukesh (Herausgeber:in) / Kumar, Sandeep (Herausgeber:in) / Bansal, Jagdish Chand (Herausgeber:in) / Kavitha, N. (Autor:in) / Chandrappa, D. N. (Autor:in)

    Kongress:

    Congress on Intelligent Systems ; 2020 ; New Delhi, India September 05, 2020 - September 06, 2020



    Erscheinungsdatum :

    20.05.2021


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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