State-of-the-art advanced driver assistance systems (ADAS) typically focus on single tasks and therefore, have functionalities with clearly defined application areas. Although said ADAS functions (e.g. lane departure warning) show good performance, they lack general usability, as e.g. different modes of operation for highways and country roads. This paper presents a real-time capable approach, which classifies the driving scene by using the newly developed Hierarchical Principal Component Classification (HPCC). Based on that, an ADAS gets information about the current scene context and is able to activate different operation modes. Exemplarily, the algorithm was trained on three different categories (highways, country roads, and inner city), but can be applied to any number and type of categories. Evaluation results on 9000 images show the reliability of the approach and mark it as a crucial step towards more sophisticated high level applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Image-based classification of driving scenes by Hierarchical Principal Component Classification (HPCC)


    Contributors:


    Publication date :

    2009-06-01


    Size :

    5450175 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Image-based Classification of Driving Scenes by Hierarchical Principal Component Classification (HPCC)

    Kastner, R. / Schneider, F. / Michalke, T. et al. | British Library Conference Proceedings | 2009


    ARPA HPCC Benchmarks

    Blank, T. | British Library Conference Proceedings | 1994



    Principal component analysis-based learning for preceding vehicle classification

    Mangai, Muthulingam Alarmel / Gounden, Nanjappagounder Ammasai | IET | 2014

    Free access

    Principal component analysis‐based learning for preceding vehicle classification

    Mangai, Muthulingam Alarmel / Gounden, Nanjappagounder Ammasai | Wiley | 2014

    Free access