Illegally parked vehicle detection using adaptive dual background model

Abstract

Detecting an illegally parked vehicle in urban scenes of traffic monitoring system becomes more complex task due to occlusions, lighting changes, and other factors. In this paper, a new framework to detect illegally parked vehicle using dual background model subtraction is presented. In our system, the adaptive background model is generated based on statistical information of pixel intensity that robust against lighting condition. Foreground analysis using geometrical properties is then applied in order to filter out false region. Vehicle detection is then integrated to verify the region as vehicle or not. Vehicle detection method is performed based on Scalable Histogram of Oriented Gradient feature and is trained using Support Vector Machine. The robustness and efficiency of the proposed method are tested on i-LIDS datasets. These are also tested using our own dataset, ISLab dataset. The test and evaluation result show that our method is efficient and robust to detect illegally parked vehicle in traffic scenes. Thus, it is very useful for traffic monitoring application system.