Improvement in the performance of camera based vehicle detector for parking lot

Abstract

The most prevailing approach now for parking lot vehicle detection system is to use sensor-based techniques such as ultrasound and infrared-light sensors. A few engineering firms provide camera-based systems, which are only for underground and indoor parking lots due to the poor accuracy of the detector. The main impediments to the camera-based system in applying to outdoor parking lots are adherent rain drops on the lens in the rain, glaring sun light and dark shadows in the daytime, and low-light intensity and back-lighting in the nighttime. To date, no camera-based detecting systems for outdoor parking lots have been in practical use. This paper reports on the performance of the detector based on the fuzzy c-means (FCM) clustering and the hyperparameter tuning by particle swarm optimization (PSO). The new system was introduced to an underground parking lot in Tokyo in early October 2009 and achieved the detection rate (sensitivity/specificity) of 99.9%. The system was also tested at an outdoor (rooftop) parking lot for a period of two months and achieved 99.6%. The performance clearly surpassed the initial goal of the project. In terms of classification accuracy, the FCM classifier is better than the support vector machine (SVM) and the computation time for training is an order of magnitude smaller than that of SVM.