2010 Volume 22 Issue 5 Pages 599-608
The most prevailing approach now for parking lot vehicle detection system is to use sensor-based techniques such as ultrasound and infrared-light sensors, though surveillance cameras have been installed in many parking lots. Camera-based systems can be used both for security/surveillance and for the management of vehicles. But in practice, the camera-based system is used only for underground and indoor parking lots due to the poor accuracy of the detection system. This paper proposes to improve the performance of the camera-based vehicle detection system by using the classifier based on fuzzy c-means (FCM) clustering. For reducing computation, the semi-hard clustering approach is applied and the hyper-parameters are tuned by particle swarm optimization (PSO). The new system was introduced to an underground parking lot at Ginza, Tokyo in October 2009. The system was also tested at an outdoor (rooftop) parking lot for a period of two months and achieved the detection rate (sensitivity) of 99.6%. The performance clearly surpassed the initial goal of the project.
