Framework for Freeway Auto-Surveillance from Traffic Video

Video based surveillance systems have been widely used on freeway for traffic monitoring, as the cameras can provide the most intuitionistic information. In order to manage all the traffic videos automatically, in this paper, a distributed real-time auto-surveillance system is presented. The freeway traffic videos are taken as input video from Pan Tilt Zoom (PTZ) camera, and then produces an analysis of the states and activity of the vehicles in the region of interested (ROI), if there is any abnormal instance, an alarm and corresponding traffic video are sent to awake surveillants. To achieve this functionality, our system relies on three main procedures. The first one initializes the system. It detects the ROI of the scene, and performs the camera calibration to remove the perspective effect of the incoming image. The second one segments moving vehicles from the images, eliminate shadow and tracks them real-time. In the third procedure, activities of vehicles are analyzed based on a series of preset situations which would happen on freeway. The detail information of each vehicle and the global statistical information are checked to find out any abnormal instance, and then triggered an alarm. We present details of the system, together with experiment results which demonstrate the accuracy and time responses.