Effective Lane Detection and Tracking Method Using Statistical Modeling of Color and Lane Edge-Orientation

This paper proposes an effective lane detection and tracking method using statistical modeling of lane color and edge-orientation in the image sequence. At first, we will address some problem of classifying a pixel into two classes(lane or background) and detecting one exact lane. Generally, the probability of a pixel classification error conditioned on the distinctive feature vector can be decreased by selecting more distinctive features. A proposed pixel classifier model(Bayes decision rule for minimizing the probability of error) uses two distinctive features, lane color and edge-orientation, for classifying a lane pixel from background image. By estimating PDFs of each feature and continuously updating the estimated PDFs, we can effectively adapt the various road conditions and the different types of lane. The histogram of edge magnitudes with respect to edge-orientation will be used as the PDF for the lane edge orientation feature. Similarly, the color histogram of the HSV color model will be used as the PDF of the color feature. And, for the postprocessing, we will use the LMS algorithm in order to exclude misclassified pixels and decide one optimal lane position. Various comparative experimental results show that the proposed scheme is very effective in the lane detection and can be implemented in real-time.