Learning from Having Learned: An Environment-adaptive Parking Space Detection Method

Abstract

Although parking space detection is a classic application in the field of image processing, most of commonly used methods can only guarantee their accuracy of detecting standard parking spaces due to the limitation of environmental diversity. Inspired by the close connection between vehicles and parking spaces in the parking environment, we believe that well-trained vehicle detection method can help improve the environmental adaptability of the parking space detection method. In this paper, we propose an environment-adaptive available parking space detection method. Based on the detection results obtained by vehicle detection and orientation estimation, our method enables the vision-only autonomous vehicle to learn environmental information near parked cars, and to detect available parking spaces accordingly. Results from real-world experiments have shown the functionality of the presented approach.