We propose to model the traffic flow in a video using a holistic generative model that does not require segmentation or tracking. In particular, we adopt the dynamic texture model, an auto-regressive stochastic process, which encodes the appearance and the underlying motion separately into two probability distributions. With this representation, retrieval of similar video sequences and classification of traffic congestion can be performed using the Kullback-Leibler divergence and the Martin distance. Experimental results show good retrieval and classification performance, with robustness to environmental conditions such as variable lighting and shadows.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Classification and retrieval of traffic video using auto-regressive stochastic processes


    Contributors:


    Publication date :

    2005-01-01


    Size :

    1117034 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification and Retrieval of Traffic Video Using Auto-Regressive Stochastic Processes

    Chan, A. B. / Vasconcelos, N. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2005



    Practical generation of video textures using the auto-regressive process

    Campbell, N. / Dalton, C. / Gibson, D. et al. | British Library Online Contents | 2004


    Auto-Regressive Model with Exogenous Input (ARX) Based Traffic Flow Prediction

    Ying, Jun / Dong, Xin / Li, Bowei et al. | ASCE | 2021