Research On Monocular Vision-based Lane Detection And Tracking

Lane detection and tracking play an important role on Intelligent transportation system, Autonomous vehicle and Driver assistance system. Roads consist of structured and unstructured roads; structured road has obvious lane markings while unstructured road without these lane markings. Monocular vision-based lane detection methods are present in this paper. Three different lane detection and tracking methods are proposed, these research can be summarized as:First, GrabCut algorithm with shape priori is proposed for road region estimation. Gaussian mixture model is applied for color distribution estimation of road region. Shape priori is adopted to refine the segmentation results using GrabCut and update the parameters of Gaussian mixture model of road region in each iteration. Shape priori could reduce the iteration numbers and improve the performance of road segmentation. An adaptive mean-shift local peak estimation method is proposed for peak detection in Hough coordinate for shape description.Second, TLD based lane marking detection and tracking methods are proposed for structured road detection. A machine learning based lane marking detector is intro-duced, and laneHAAR and laneHOG features are proposed for describe the pattern and structure of lane markings, separately. After that, the laneCOMB features combined with the color and structure information are used for classification. In consideration of the time requirement of lane detection, the cascade classifier is selected for lane marking classification. Furthermore, the lane tracking method is given based on TLD framework for long time lane tracking. PN learning combined with lane symmetric, lane width and lane color constraints are used for online training. The prediction meth-ods also applied for reducing the detection region in lane tracking.Third, Parallel-snake model with balloon force is proposed for road detection. Parallel-snake model could handle both structure and unstructured road under same framework as it based on the gradient of image other than painted markings. The continuous constraint on the head and tail of snake is removed and stretching for is introduced to produce an open snake instead of closed curve. The captured image is transformed into bird-view using inverse perspective mapping to retrieve the parallel property of road boundaries, then parallel constraint is introduced into two open snakes to produce a Parallel-snake which could be deformed on bird-view image for lane de-tection. A balloon force is also employed which could guarantee Parallel-snake could convergence on the lane region with small gradient. Furthermore, Markov-chain is ap-plied to model the covariance between contiguous frames and Kalman filter is used for parameters prediction and refinement.