An Efficient Scheme to Obtain Background Image in Video for YOLO-based Static Object Recognition

Abstract

Detecting backgrounds in videos is an important technology that can be used for many applications such as management of major facilities and military surveillance depending on the purpose. It is difficult to accurately find and identify important objects in the background if there are obstacles such as pedestrian or car in the video. In order to overcome this problem, the following method is used to detect the background. First, a pixel area histogram is generated to determine the amount of change in pixel units of an image over time. Based on the histogram, we propose an algorithm that estimates the background by selecting the case with the smallest rate of change. In addition, in order to strongly respond to changes in the surrounding environment, even when a change in brightness occurs, this is solved through frame overlap. Finally, the desired object is identified by applying YOLO v3 as a model for object detection in the obtained background. Through the above process, this study proposes a method for effectively identifying static objects in the background by precisely estimated background of the video. Experimental results show that the non-detection and false detection rate for the background object is enhanced by 60.2% and 11.2%, respectively, in comparison with when the proposed method was not applied.