Pedestrian-vehicle conflict can reflect potential hazard in mixed traffic. Automatic pedestrian-vehicle conflict detection algorithms can provide both real-time warning information to traffic users and collect data for further analysis. This paper proposes a method to automatically classify different traffic users and to detect pedestrian-vehicle conflict using video data. A mixed Gauss-based background differencing algorithm is used to detect a foreground target. The speed and area parameters are used to classify traffic objects. A tracking algorithm based on multi-feature fusion is designed to track classified objects. The notion of pedestrian-vehicle conflict is adjusted for video-based detection, and a pedestrian-vehicle conflict video detection model is designed. In total, 117 pedestrian-vehicle conflicts are collected. The results show that the proposed method can correctly detect about 80.3% of pedestrian-vehicle conflicts. Further research will focus on the improvement of the object extraction algorithm, and more mixed traffic scenes will be tested based on this proposed method.


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    Titel :

    Real-Time Pedestrian-Vehicle Conflict Detection Algorithm Using Video Data


    Beteiligte:
    Liu, Shuai (Autor:in) / Zhu, Tao (Autor:in) / Zhang, Yingying (Autor:in) / Yao, Danya (Autor:in)

    Kongress:

    14th COTA International Conference of Transportation Professionals ; 2014 ; Changsha, China


    Erschienen in:

    CICTP 2014 ; 535-546


    Erscheinungsdatum :

    2014-06-24




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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