Real-time lane detection using spatio-temporal incremental clustering

In this paper, we propose a novel method for lane detection in real-time based on unsupervised learning using images from a camera mounted on the dashboard of a vehicle. Lane detection is a crucial element of an intelligent vehicle safety system. Lane departure warning systems rely on accurate detection of lanes and are important for a driver assistant system. In our system, we extract the features of interest and detect the lane markings or foreground regions in each frame. We develop a spatio-temporal incremental clustering algorithm coupled with curve fitting for detecting the lanes on-the-fly. The spatio-temporal incremental clustering is performed over the detected foreground region for finding the lanes accurately. Each cluster represents a lane across space and time. The lanes are then identified by fitting curves on these clusters. Our system is capable of accurately detecting straight and curved lanes, non-continuous and continuous lanes and is independent of number of lanes in the frame and the system is fast because we have not used any database for processing the images. Experimental results show that our algorithm can accurately perform lane detection in challenging scenarios.