Vacant Parking Space Detection Based on Plane-based Bayesian Hierarchical Framework

In this paper, we propose a vacant parking space detection system that operates day and night. In the daytime, the major challenges of the system include dramatic lighting variations, shadow effect, inter-object occlusion, and perspective distortion. In the nighttime, the major challenges include insufficient illumination and complicated lighting conditions. To overcome these problems, we propose a plane-based method which adopts a structural 3-D parking lot model consisting of plentiful planar surfaces. The plane-based 3-D scene model plays a key part in handling inter-object occlusion and perspective distortion. On the other hand, to alleviate the interference of unpredictable lighting changes and shadows, we propose a plane-based classification process. Moreover, by introducing a Bayesian hierarchical framework to integrate the 3-D model with the plane-based classification process, we systematically infer the parking status. Last, to overcome the insufficient illumination in the nighttime, we also introduce a preprocessing step to enhance image quality. The experimental results show that the proposed framework can achieve robust detection of vacant parking spaces in both daytime and nighttime.