Automatic vision-based parking slot detection and occupancy classification

• Published in: Expert systems with applications 2023-09, Vol.225, p.120147, Article 120147
• Main Authors: Grbić, Ratko, Koch, Brando
• Format: Article
• Language: English
• Subjects: CNRPark-EXT; Deep learning; Parking occupancy; Parking slot detection; PKLot; Vehicle detection
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.120147

Parking guidance information (PGI) systems are used to provide information to drivers about the nearest parking lots and the number of vacant parking slots. Recently, vision-based solutions started to appear as a cost-effective alternative to standard PGI systems based on hardware sensors mounted on each parking slot. Vision-based systems provide information about parking occupancy based on images taken by a camera that is recording a parking lot. However, such systems are challenging to develop due to various possible viewpoints, weather conditions, and object occlusions. Most notably, they require manual labeling of parking slot locations in the input image which is sensitive to camera angle change, replacement, or maintenance. In this paper, the algorithm that performs Automatic Parking Slot Detection and Occupancy Classification (APSD-OC) solely on input images is proposed. Automatic parking slot detection is based on vehicle detections in a series of parking lot images upon which clustering is applied in bird’s eye view to detect parking slots. Once the parking slots positions are determined in the input image, each detected parking slot is classified as occupied or vacant using a specifically trained ResNet34 deep classifier. The proposed 2-step approach is extensively evaluated on well-known publicly available datasets (PKLot and CNRPark+EXT), showing high efficiency in parking slot detection and certain degree of robustness to the presence of illegal parking or passing vehicles. Trained classifier achieves high accuracy in parking slot occupancy classification. •An efficient approach to automatic parking slot detection.•The approach removes the need for manual labeling of parking slots.•The approach is resistant to passing and illegally parked vehicles.•Precision and recall of parking slot detection grow with the number of used images.