Traffic congestion is a widespread problem. Dynamic traffic routing systems and congestion pricing are getting importance in recent research. Lane prediction and vehicle density estimation is an important component of such systems. We introduce a novel problem of vehicle self positioning which involves predicting the number of lanes on the road and vehicle's position in those lanes using videos captured by a dashboard camera. We propose an integrated closed-loop approach where we use the presence of vehicles to aid the task of self-positioning and vice versa. To incorporate multiple factors and high-level semantic knowledge into the solution, we formulate this problem as a Bayesian framework. In the framework, the number of lanes, the vehicle's position in those lanes and the presence of other vehicles are considered as parameters. We also propose a bounding box selection scheme to reduce the number of false detections and increase the computational efficiency. We show that the number of box proposals decreases by a factor of 6 using the selection approach. It also results in large reduction in the number of false detections. The entire approach is tested on real-world videos and is found to give acceptable results.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Improving Vision-Based Self-Positioning in Intelligent Transportation Systems via Integrated Lane and Vehicle Detection


    Beteiligte:
    Chandakkar, Parag S. (Autor:in) / Wang, Yilin (Autor:in) / Li, Baoxin (Autor:in)


    Erscheinungsdatum :

    01.01.2015


    Format / Umfang :

    415934 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Improving GPS-based vehicle positioning for Intelligent Transportation Systems

    Amini, Arghavan / Vaghefi, Reza Monir / de la Garza, Jesus M. et al. | IEEE | 2014


    IMPROVING GPS-BASED VEHICLE POSITIONING FOR INTELLIGENT TRANSPORTATION SYSTEMS

    Amini, A. / Vaghefi, R. / Garza, J. et al. | British Library Conference Proceedings | 2014


    Vision Based Lane detection for Active Security in Intelligent Vehicle

    Tian, Min / Liu, Fuqiang / Zhu, Wenhong et al. | IEEE | 2006