Advancement Through Research
A comprehensive ITS (Intelligent Transport Systems) needs a dynamic parking information system giving real-time information about the number of parking space available in each car park. Computer vision has been potentially an important element in the foundation of integrated traffic surveillance and control systems. The objective of this study is to propose a robust and reliable method for detecting a vehicle and vacancy simultaneously in a car park using image processing techniques and a neural network model which is claimed to be more powerful than many expert systems.
In this study, a neural network model 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 and different gray levels in the image in order to find good input vectors to the network and pre-processing for reducing computing time. The results show that the method for vacancy and vehicle detection proposed in the paper is efficient for the noise, and therefore has the potential for parking data collection. In addition, the proposed method could be the framework for numerous traffic applications, such as congestion and incident detection, and vehicle classification with resulting improvements in performance to the method currently used.
Intelligent Transport Systems, Dynamic parking information, Computer vision, Neural network, Incident detection, Vehicle classification
