This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.


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    Titel :

    Recognition of Car Makes and Models From a Single Traffic-Camera Image


    Beteiligte:
    He, Hongsheng (Autor:in) / Shao, Zhenzhou (Autor:in) / Tan, Jindong (Autor:in)


    Erscheinungsdatum :

    01.12.2015


    Format / Umfang :

    2296454 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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