Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Monocular precrash vehicle detection: features and classifiers


    Beteiligte:
    Sun, Zehang (Autor:in) / Bebis, G. (Autor:in) / Miller, R. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2006


    Format / Umfang :

    16 Seiten, 45 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    Precrash-sensors

    Spies, H. | British Library Conference Proceedings | 1996


    A real-time precrash vehicle detection system

    Zehang Sun, / Miller, R. / Bebis, G. et al. | IEEE | 2002


    A Real-Time Precrash Vehicle Detection System

    Sun, Z. / Miller, R. / Bebis, G. et al. | British Library Conference Proceedings | 2002


    Precrash-Sensoren

    Spies, H. | Tema Archiv | 1996


    Verification of Precrash Sensor System

    Kohn, P. N. / Varchmin, J.-U. / VDI | British Library Conference Proceedings | 2003