Tracking vehicles is an important and challenging issue in video-based intelligent transportation systems and has been broadly investigated in the past. This paper presents a robust and real-time method for tracking vehicles and the proposed algorithm includes two stages: vehicle detection, vehicle tracking. Vehicle detection is a key step and the concept of tracking vehicle is built upon the vehicle-segmentation method. According to the segmented vehicle shape, we propose a three-step prediction method based on the Kalman filter to track each vehicle. The proposed method has been tested on a number of monocular traffic-image sequences and the experimental results show that the algorithm is robust and real-time. The correct rate of vehicle tracking is higher than 85 percent, independent of environmental conditions.


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    Titel :

    Real-time vehicles tracking based on Kalman filter in a video-based ITS


    Beteiligte:
    Xie, Lei (Autor:in) / Zhu, Guangxi (Autor:in) / Wang, Yuqi (Autor:in) / Xu, Haixiang (Autor:in) / Zhang, Zhenming (Autor:in)


    Erscheinungsdatum :

    2005


    Format / Umfang :

    4 Seiten, 8 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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