Abstract
In this paper, we propose a parking slot markings detection method based on the geometric features of parking slots. The proposed system mainly consists of two steps, namely, separating line detection and parking slot entrance detection. First, in the separating line detection stage, we propose a line-clustering method based on the line segment detection (LSD) algorithm. Our detecting and line-clustering algorithm can detect the separating lines that contain a pair of parallel lines with a fixed distance in a bird's eye view (BEV) image under diverse lighting and ground conditions. Consequently, parking slot candidates are generated by pairing the separating lines according to the width of the parking slots. In the parking slot entrance detection process, we propose a multiview fusion-based learning approach that can increase the number of training samples by performing a perspective transformation on the acquired BEV images. The proposed method was evaluated using 353 BEV images covering diverse parking slot markings. Experiments show that the proposed method can recognize typical perpendicular and parallel rectangular parking slots, and a precision of 97.4% and recall of 96.6% are achieved.