Surveillance video cameras have been increasingly deployed along roadways over the past decade. Automatic traffic data collection through surveillance video cameras is highly desirable; however, sight-degrading factors and camera vibrations make it an extremely challenging task. In this paper, a computer-vision–based algorithm for vehicle detection and tracking is presented, implemented, and tested. This new algorithm consists of four steps: user initialization, spatiotemporal map generation, strand analysis, and vehicle tracking. It relies on a single, environment-insensitive cue that can be easily obtained and analyzed without camera calibration. The proposed algorithm was implemented in Microsoft Visual C++ using OpenCV and Boost C++ graph libraries. Six test video data sets, representing a variety of lighting, flow level, and camera vibration conditions, were used to evaluate the performance of the new algorithm. Experimental results showed that environmental factors do not significantly impact the detection accuracy of the algorithm. Vehicle count errors ranged from 8% to 19% in the tests, with an overall average detection accuracy of 86.6%. Considering that the test scenarios were chosen to be challenging, such test results are encouraging.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Video-Based Vehicle Detection and Tracking Using Spatiotemporal Maps


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Malinovskiy, Yegor (Autor:in) / Wu, Yao-Jan (Autor:in) / Wang, Yinhai (Autor:in)


    Erscheinungsdatum :

    01.01.2009




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Detection of Parked Vehicles Using Spatiotemporal Maps

    Albiol, A. / Sanchis, L. / Mossi, J. M. | IEEE | 2011


    Commercial Vehicle Route Tracking using Video Detection

    S. Cheng / R. C. Huck / P. Verma et al. | NTIS | 2010


    Spatiotemporal Vehicle Tracking, Counting and Classification

    Ramanathan, Abirami / Chen, Min | IEEE | 2017


    Anomalous video event detection using spatiotemporal context

    Jiang, F. / Yuan, J. / Tsaftaris, S. A. et al. | British Library Online Contents | 2011