The authors present a novel approach using 2D spatio-temporal images for automatic traffic monitoring. A TV camera is mounted above the highway to monitor the traffic through two slice windows for each traffic lane. One slice window is along the lane and the other perpendicular to the lane axis. Two types of 2D spatio-temporal (ST) images are used in the system: the panoramic view image (PVI) and the epipolar plane image (EPI). The real-time vision system for automatic traffic monitoring, VISATRAM, an inexpensive system with a PC 486 and an image frame grabber has been tested with real road images. Not only can the system count the vehicles and estimate their speeds, but it can also classify the passing vehicles using 3D measurements (length, width and height). The VISATRAM works robustly under various light conditions including shadows in the day and vehicle lights at night, and automatically copes with the gradual and abrupt changes of the environment.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A real-time vision system for automatic traffic monitoring based on 2D spatio-temporal images


    Beteiligte:
    Zhigang Zhu (Autor:in) / Bo Yang (Autor:in) / Guangyou Xu (Autor:in) / Dingji Shi (Autor:in)


    Erscheinungsdatum :

    01.01.1996


    Format / Umfang :

    661592 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Real-Time Vision System for Automatic Traffic Monitoring Based on 2D Spatio-Temporal Images

    Zhu, Z. / Yang, B. / Xu, G. et al. | British Library Conference Proceedings | 1996


    VISION BASED REAL TIME TRAFFIC MONITORING

    KANHERE NEERAJ KRANTIVEER / BIRCHFIELD STANLEY T / SARASUA WAYNE A | Europäisches Patentamt | 2020

    Freier Zugriff


    Knowledge Distillation-Based Spatio-Temporal MLP Model for Real-Time Traffic Flow Prediction

    Zhang, Junfeng / Xie, Cheng / Cai, Hongming et al. | IEEE | 2024


    Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach*

    Kose, Neslihan / Kopuklu, Okan / Unnervik, Alexander et al. | IEEE | 2019