Directional-DBSCAN: Parking-slot detection using a clustering method in around-view monitoring system

Parking slot detection algorithms using visual sensors have been required for various automated parking assistant systems. In most previous studies, popular feature detectors, such as the Harris corner or the Hough line detector, have been employed for detecting parking slots. However, these algorithms were originally designed to find distinct features and are inadequate for the short, curvy, faint and distorted parking-lines of long-range surround-view images, especially in around-view monitoring systems. In this paper, we propose a robust parking slot detection algorithm based on the line-segment-level clustering method. The proposed algorithm consists of line-segment detection with the proposed Directional-DBSCAN line-level feature-clustering algorithm and slot detection with slot pattern recognition. In comparison to other feature detectors, we show that the Directional-DBSCAN algorithm robustly extracts lines even when they are short and faint. Moreover, we verify that the parking-slot detection algorithm with pattern recognition can be applicable to diverse slot types and environments with experiments on abundant dataset.