This paper describes research to classify road vehicles into a range of broad categories using simple measures of size and shape derived from view-dependent binary silhouettes using images derived from a static roadside CCTV camera. A novel approach to camera calibration utilizes calibrated images mapped by Google Earth to provide accurately-surveyed scene geometry that is manually corresponded with visible groundplane landmarks in the CCTV images. In the experiments reported here, manual segmentation is used to delineate vehicles in the images and a set of scaled features is extracted from each binary silhouette. Classification assigns each blob to one of four vehicle classes (car, van, bus and bicycle/motorcycle) using two feature-based classifiers (SVM and random forests) and a model-based approach. Results are presented for 10-fold cross validation study involving over 2000 manually labeled silhouettes. A peak classification performance of 96.26% is observed for SVM.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vehicle type categorization: A comparison of classification schemes


    Beteiligte:
    Zezhi Chen, (Autor:in) / Ellis, T. (Autor:in) / Velastin, S. A. (Autor:in)


    Erscheinungsdatum :

    01.10.2011


    Format / Umfang :

    1043023 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Connectionist Approach to Improving Highway Vehicle Classification Schemes

    Kwigizile, Valerian / Mussa, Renatus N. / Selekwa, Majura | Transportation Research Record | 2005


    Length-Based Vehicle Classification Schemes and Length Bin Boundaries

    Weinblatt, Herbert / Minge, Erik / Petersen, Scott | Transportation Research Record | 2013


    Handover schemes in space networks: classification and performance comparison

    Chowdhury, P.K. / Atiquzzaman, M. / Ivancic, W. | IEEE | 2006