In this paper, the framework is presented for using active learning to train a robust monocular on-road vehicle detector for active safety, based on Adaboost classification and Haar-like rectangular image features. An initial vehicle detector was trained using Adaboost and Haar-like rectangular image features and was very susceptible to false positives. This detector was run on an independent highway dataset, storing true detections and false positives to obtain a selectively sampled training set for the active learning training iteration. Various configurations of the newly trained classifier were tested, experimenting with the trade-off between detection rate and false detection rate. Experimental results show that this method yields a vehicle classifier with a high detection rate and low false detection rate on real data, yielding a valuable addition to environmental awareness for intelligent active safety systems in vehicles.


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    Titel :

    Active learning based robust monocular vehicle detection for on-road safety systems


    Beteiligte:


    Erscheinungsdatum :

    01.06.2009


    Format / Umfang :

    4730274 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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