Automatic lane detection from vehicle motion trajectories

Lane detection is important in intelligent transportation systems. This paper presents a novel algorithm for vehicle motion trajectory and lane boundary detection that uses Gaussian mixture model-based background subtraction and active contours. The algorithm uses an adaptive GMM that can cope with sudden illumination changes for detecting moving vehicles (resulting in a road score map, RSmap), followed by a Kalman filter tracker to generate pixel-level motion vectors. A novel active contour energy expression based on the accumulation of motion trajectories and the spatio-temporal RSmaps is used to detect lane boundaries. Experimental results are presented for video from a real road scene to show the effectiveness of the proposed algorithm, without the need for road lane markings.