Detecting abandoned objects in crowded scenes of surveillance videos using adaptive dual background model
Wahyono Wahyono, Alexander Filonenko, Kang-Hyun Jo · 2015
Detecting an abandoned object in crowded scenes of surveillance videos becomes more complex task due to occlusions, lighting changes, and other factors. In this paper, a new framework to detect abandoned object 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. Human and vehicle detection are then integrated to verify the region as static object, human or vehicle. The robustness and efficiency of the proposed method are tested on several public databases such as i-LIDS and PETS2006 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 abandoned object in crowded scenes.