This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, blob level and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. Kalman filtering is used to estimate vehicle parameters. The proposed method is based on the establishment of correspondences among blobs and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided, which demonstrate the effectiveness of the method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vision-based vehicle classification


    Contributors:


    Publication date :

    2000-01-01


    Size :

    730243 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vision-Based Vehicle Classification

    Gupte, S. / Masoud, O. / Papanikolopoulos, N. P. et al. | British Library Conference Proceedings | 2000


    Vision-based approach for urban vehicle detection & classification

    Pham, Long Hoang / Duong, Tin Trung / Tran, Ha Manh et al. | IEEE | 2013


    Algorithm for vision-based vehicle detection and classification

    Hu, Youpan / He, Qing / Zhuang, Xiaobin et al. | IEEE | 2013



    Computer Vision System for Automatic Vehicle Classification

    Yuan, X. / Lu, Y.-J. / Sarraf, S. | British Library Online Contents | 1994