Intelligent Detection of Missing and Unattended Objects in Complex Scene of Surveillance Videos

This study proposes a method to detect and mark the target object removed from the monitoring scene and the unknown object left in the monitoring scene. The present method uses the timeliness background to extract the foreground object and to mask the part that was unwanted. The foreground object was compared with the current frame, thus, the unreliable pixels were filtered out. By the identification of the center of mass (CoM) on foreground object, an object detection rule is developed to determine whether the foreground object is missing object or unattended object. In this paper, the present approach improves the problem with the high similarity of pixels between the foreground object and the background model. The experiment can be applied to any complex environment, both indoors and outdoors, such as the subway station, which is thronged with people. The experimental outcome, using the proposed method, can determine the missing and unattended object accurately and the unreasonable object is excluded in video surveillance system.