Vehicle detection at night time is a challenging problem due to low visibility and light distortion caused by motion and illumination in urban environments. This paper presents a method based on the deformable object model for detecting and classifying vehicles by using monocular infra-red cameras. As some features of vehicles, such as headlight and taillights are more visible at night time, we propose a weighted version of the deformable part model. We define weights for different features in the deformable part model of the vehicle and try to learn the weights through an enormous number of positive and negative samples. Experimental results prove the effectiveness of the algorithm for detecting close and medium range vehicles in urban scenes at night time.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models


    Beteiligte:
    Niknejad, H. T. (Autor:in) / Mita, S. (Autor:in) / McAllester, D. (Autor:in) / Naito, T. (Autor:in)


    Erscheinungsdatum :

    01.10.2011


    Format / Umfang :

    1020791 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Vision-Based Nighttime Vehicle Detection Using CenSurE and SVM

    Kosaka, Naoya / Ohashi, Gosuke | IEEE | 2015


    Near-Infrared-Based Nighttime Pedestrian Detection Using Grouped Part Models

    Lee, Yi-Shu / Chan, Yi-Ming / Fu, Li-Chen et al. | IEEE | 2015



    Nighttime driving and mesopic vision in vehicle safety

    Moura, Carlos / Moura, Clayton / Pingueiro, Paulo et al. | British Library Conference Proceedings | 2020