Middleware for Distributed Video Surveillance

protecting major facilities from terrorism and other threats. At the hardware level, standard IP networking devices and IP video cameras enable building thousand-camera networks at a reasonable cost. However, monitoring surveillance networks through human inspection is expensive and remarkably ineffective. Trained operators lose concentration and miss a high percentage of significant events after only 10 minutes. Consequently, surveillance users are turning to software for automated video surveillance. Most research in this area concentrates on the computer vision algorithms required to detect and interpret activity in video. Such work is limited to networks of less than 100 cameras. We need to address the real-world issues raised by scaling to thousands of cameras and integrating a diverse, evolving collection of surveillance approaches into continuously operating surveillance networks.