Advanced Driver Assistance Systems (ADAS) is progressing into urban traffics. Existing ADAS is concentrated on obstacle detection, mainly for pedestrian or vehicle. Limited work has been conducted on multi-class obstacle classification. It has been recognized that object classification is essential for safety applications in urban traffic. This paper addresses this issue and aims to develop an approach for simultaneous detection and classification of multi-class obstacles. In the paper, stereovision is used to segment obstacles from traffic background by using distance measure, then active contour model is adopted to extract complete contour curve of the detected obstacles. Based on the contour extracted, object features including aspect ratio, area ratio and height are integrated for classifying object types including vehicles, pedestrian and other obstacles. The approach presented here was tested on substantial complex urban traffic images and the corresponding results prove the efficiency of the approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Obstacle recognition for ADAS using stereovision and snake models


    Contributors:


    Publication date :

    2014-10-01


    Size :

    579816 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Stereovision for ADAS

    Gehrig, Stefan / Franke, Uwe | Springer Verlag | 2015


    Obstacle localization and recognition for autonomous forklifts using omnidirectional stereovision

    Costea, Arthur D. / Vatavu, Andrei / Nedevschi, Sergiu | IEEE | 2015



    Obstacle Detection in Urban Traffic Using Stereovision

    Huang, Y. / IEEE | British Library Conference Proceedings | 2005


    Obstacle detection using sparse stereovision and clustering techniques

    Kramm, Sebastien / Bensrhair, Abdelaziz | IEEE | 2012