Intelligent visual surveillance for road vehicles is a key component for developing autonomous intelligent transportation systems. In this paper, a probabilistic model for prediction of traffic accidents using 3D model based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3D model based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Vehicle activities are finally predicted by locating and matching each observed partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments with a model scene show the effectiveness of the proposed algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic accident prediction using vehicle tracking and trajectory analysis


    Contributors:
    Weiming Hu, (author) / Xuejuan Xiao, (author) / Dan Xie, (author) / Tieniu Tan, (author)


    Publication date :

    2003-01-01


    Size :

    501114 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic Accident Prediction Using Vehicle Tracking and Trajectory Analysis

    Hu, W. / Xiao, X. / Xie, D. et al. | British Library Conference Proceedings | 2003



    Traffic accident prediction method

    CHEN MING / ZHONG HUIJIAN / LIN JING et al. | European Patent Office | 2023

    Free access

    Vehicle trajectory tracking technology based on traffic monitoring network

    ZHAO QIUHONG / FAN YUJUN | European Patent Office | 2024

    Free access

    Vehicle trajectory tracking prediction system and method

    WANG XINZHI / LIU JINBO / ZHANG JIAN et al. | European Patent Office | 2023

    Free access