This paper presents a cross-verification approach to fuse radar and vision data for vehicle detection. Firstly, a realtime vision approach using specific shadow segmentation is used to detect vehicles in whole image independently. The fusion approach contains two steps: matching and validation. The targets respectively from radar and vision verify each other in matching process. Then the unmatched radar targets are validated by vision data once again. Experiment results with test dataset from real traffic scenes on freeway and urban roads are presented to illustrate the performance of this approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    On-road vehicle detection fusing radar and vision


    Beteiligte:
    Xin Liu, (Autor:in) / Zhenping Sun, (Autor:in) / Hangen He, (Autor:in)


    Erscheinungsdatum :

    01.07.2011


    Format / Umfang :

    1910015 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Obstacle detection and classification fusing radar and vision

    Bertozzi, M. / Bombini, L. / Cerri, P. et al. | IEEE | 2008


    Obstacle Detection and Classification Fusing Radar and Vision

    Bertozzi, M. / Bombini, L. / Cerri, P. et al. | British Library Conference Proceedings | 2008


    Advanced Lane Recognition - Fusing Vision and Radar

    Gern, A. / Franke, U. / Levi, P. et al. | British Library Conference Proceedings | 2000


    Advanced lane recognition-fusing vision and radar

    Gern, A. / Franke, U. / Levi, P. | IEEE | 2000


    Fusing Radar and Vision Data for Cut-In Vehicle Identification in Platooning Applications

    Liu, Mengke / Rathinam, Sivakumar / Lukuc, Mike et al. | British Library Conference Proceedings | 2020