This paper deals with real-time image processing of crowded outdoor scenes with the objective of creating an effective traffic management system that monitors urban settings (urban intersections, streets after athletic events, etc.). The proposed system can detect, track, and monitor both pedestrians (crowds) and vehicles. We describe the characteristics of the tracker that is based on a new detection method. Initially, we produce a motion estimation map. This map is then segmented and analyzed in order to remove inherent noise and focus on particular regions. Moreover, tracking of these regions is obtained in two steps: fusion and measurement of the current position and velocity, and then estimation of the next position based on a simple model. The instability of tracking is addressed by a multiple-level approach to the problem. The computed data are then analyzed to produce motion statistics. Experimental results from various sites in the Twin Cities area are presented. The final step is to provide this information to an urban traffic management center that monitors crowds and vehicles in the streets.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Monitoring crowded traffic scenes


    Contributors:


    Publication date :

    2002-01-01


    Size :

    626621 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    MONITORING CROWDED TRAFFIC SCENES

    Maurin, B. / Masoud, O. / Papanikolopoulos, N. et al. | British Library Conference Proceedings | 2002


    Inverse Universal Traffic Quality -- a Criticality Metric for Crowded Urban Traffic Scenes

    Schütt, Barbara / Zipfl, Maximilian / Zöllner, J. Marius et al. | ArXiv | 2023

    Free access

    Inverse Universal Traffic Quality - a Criticality Metric for Crowded Urban Traffic Scenes

    Schutt, Barbara / Zipfl, Maximilian / Zollner, J. Marius et al. | IEEE | 2023


    Pedestrian detection in crowded scenes

    Leibe, B. / Seemann, E. / Schiele, B. | IEEE | 2005


    Counting moving persons in crowded scenes

    Conte, D. | British Library Online Contents | 2013