Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter

ABSTRACT

We present a novel lane detection and tracking system using a fusion of Maximally Stable Extremal Regions (MSER) and Progressive Probabilistic Hough Transform (PPHT). First, MSER is applied to obtain a set of blobs including noisy pixels (e.g., trees, cars and traffic signs) and the candidate lane markings. A scanning refinement algorithm is then introduced to enhance the results of MSER and filter out noisy data. After that, to achieve the requirements of real-time systems, the PPHT is applied. Compared to Hough transform which returns the parameters ρ and Θ, PPHT returns two end-points of the detected line markings. To track lane markings, two kalman trackers are used to track both end-points. Several experiments are conducted in Ottawa roads to test the performance of our framework. The detection rate of the proposed system averages 92.7% and exceeds 84.9% in poor conditions.

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Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Object recognition