The objective of this project is to extend the services offered to the clients by the Piconet company, which is the national leader of city surface parking management systems. The aim is to develop a robust method for monitoring the parking occupancy based on processing of images captured by surveillance cameras. This method has to adapt to harsh weather conditions and to changing in illumination due to some natural causes like clouds or artificial ones like public lighting. It also has to adapt to a large variety of cameras’ deployment angles, and to learn environment changes. It has to offer a good support for future extension of services – e.g. automatic car plates recognition or automatic payment methods.
The company plan to use the method developed here to implement a mobile application that can offer an overview of parking occupancy to the clients for an entire area managed by the company. The utility of this solution is to save the client time spent in finding a parking place, especially in crowded central city areas, and it was already requested by many existing clients. Moreover, it will ensure some other benefits like saving the fuel consumption and reducing the pollution generated by cars that creates overhead traffic on the way to find a free parking place. In order to ensure the desired accuracy this method will combine image processing algorithms with existing statistical information collected by the company, and with learned data. In addition it should include a simple and clear procedure for further system deployment.
The result of research will be a functional prototype that can be integrated in the existing company parking management system.