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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    Title :

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


    Contributors:
    Xie, Lei (author) / Zhu, Guangxi (author) / Wang, Yuqi (author) / Xu, Haixiang (author) / Zhang, Zhenming (author)


    Publication date :

    2005


    Size :

    4 Seiten, 8 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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