Vehicle classification is an important subject of study due to its significance in a number of areas including law enforcement, traffic surveillance, autonomous navigation, and transportation management. While numerous approaches have been proposed, few studies have been published with regard to the multi-view classification of vehicles captured in real surveillance. In this paper, we consider the multi-view classification of vehicles as an attribute prediction problem with views (rear, front, and side) as attributes. The corresponding multi-task learning is implemented in the Region-based Convolutional Neural Network (RCNN) framework, which classifies vehicle categories (car, truck, bus, and van) and predicts the attributes simultaneously. Experiments on a field-captured vehicle dataset provide satisfactory results, with approximate 83% accuracy for vehicle type classification and over 90% accuracy for attribute prediction.


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    Titel :

    Vehicle type classification and attribute prediction using multi-task RCNN


    Beteiligte:
    Huo, Zhuoqun (Autor:in) / Xia, Yizhang (Autor:in) / Zhang, Bailing (Autor:in)


    Erscheinungsdatum :

    01.10.2016


    Format / Umfang :

    827076 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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