A comprehensive ITS needs a dynamic parking information system giving real-time information about the number of parking spaces available in each car park. Computer vision is a potentially important element in the foundation of integrated traffic surveillance and control systems. The objective of this research is to propose a robust and reliable method for detecting a vacancy in a car park using image processing techniques and a neural network model. Fuzzy ARTMAP, which is a supervised and self-organizing system, is used for recognizing different patterns that come from complicated real scenes of a car park. The experiments have been done with the gray values of pixels on different image sizes, different gray levels in the image, and edge information. Two input properties - edge information and gray value of each pixel - have been combined in the input vectors to get better results. The results show that the method for vacancy detection proposed in the paper is efficient for the noise, partial occlusion and perspective problems which are inevitable in the real world image. In addition, the proposed method could provide a framework for numerous traffic applications, such as congestion and incident detection, and vehicle classification.
Abstract