Autonomous Detection and Imaging of Abandoned Luggage in Real World Environments.

摘要

This Trident Project developed a system that is able to detect and produce high resolution imagery of unattended items in a crowded scene, such as an airport, using live video processing techniques. Video surveillance is commonplace in today's public areas, but as the number of cameras increases, so do the human resources required to monitor them. Additionally, current surveillance networks are restricted by the low resolution of their cameras. For example, while there is an extensive security camera network in the London Underground, its low resolution prevented it from being used to automatically identify the terrorists that entered the train stations in July 2005. With this in mind, this project developed a surveillance system that is able to autonomously monitor a scene for suspicious events by combining a low resolution camera for surveillance (a webcam) with a moving high resolution camera (a 6 mega-pixel digital still-frame camera) to provide a greater level of detail. This enhanced capability is used to determine whether or not the event is a threat. For the purposes of this research, suspicious events were defined as a person leaving a piece of luggage unattended for an extended period of time. Initial analysis of the surveillance video involved separating the foreground (such as people carrying luggage) from the background. In order to do this using live video, an automated algorithm was developed which creates a composite background image from a small number of video frames. In the algorithm, areas detected as motion were removed from individual frames.