Region-of-interest generation for lane detection using high-level information

Vision-based lane detection is still a challenging task in real application with a variety of complex road scenes. What makes the problem even more difficult is the requirements of real time implement with on-board processor. To speedup the lane detection processing, the Region-of-Interesting (ROI) method was widely used to narrow the candidate searching range for local road features. In this paper, a continuous form of the ROI is first deduced from the lane model and the perspective projection. Then the factors that contribute to the appearance of the ROI are discussed. Lastly, an approximated form of the ROI is given for fast computing. In the proposed model, ROI is considered as a probability map, in which the value is corresponding to the possibility of the lane-markings to appear in a certain location in the scene. Moreover, the model takes the running manner of the vehicle and vibration of the vehicle into account. Experiments show that the proposed algorithm is very efficient in removing the outliers of local features in complex road scenes.