In this paper, we present improved lane tracking using vehicle localization. Lane markers are detected using a bank of steerable filters, and lanes are tracked using Kalman filtering. On-road vehicle detection has been achieved using an active learning approach, and vehicles are tracked using a Condensation particle filter. While most state-of-the art lane tracking systems are not capable of performing in high-density traffic scenes, the proposed framework exploits robust vehicle tracking to allow for improved lane tracking in high density traffic. Experimental results demonstrate that lane tracking performance, robustness, and temporal response are significantly improved in the proposed framework, while also tracking vehicles, with minimal additional hardware requirements.


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    Title :

    Improved vision-based lane tracker performance using vehicle localization


    Contributors:
    Sivaraman, S (author) / Trivedi, M M (author)


    Publication date :

    2010-06-01


    Size :

    634187 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Improved Vision-Based Lane Tracker Performance Using Vehicle Localization, pp. 676-681

    Sivaraman, S. / Trivedi, M.M. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2010



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