There are immediate needs for automated surveillance systems in commercial, law enforcement and military applications. Mounting video cameras is cheap, but finding available human resources to observe the output is expensive. Although surveillance cameras are already prevalent in banks, stores, and parking lots, video data currently is used only "after the fact" as a forensic tool, thus losing its primary benefit as an active, real-time medium. What is needed is continuous 24-hour monitoring of surveillance video to alert security officers to a burglary in progress, or to a suspicious individual loitering in the parking lot, while there is still time to prevent the crime. In addition to the obvious security applications, video surveillance technology has been proposed to measure traffic flow, detect accidents on highways, monitor pedestrian congestion in public spaces, compile consumer demographics in shopping malls and amusement parks, log routine maintainence tasks at nuclear facilities, and count endangered species. The numerous military applications include patrolling national borders, measuring the flow of refugees in troubled areas, monitoring peace treaties, and providing secure perimeters around bases and embassies.
In 1997, the Defense Advanced Research Projects Agency (DARPA) Information Systems Office began a three-year program to develop Video Surveillance and Monitoring (VSAM) technology. The objective of the VSAM project was to develop automated video understanding technology for use in future urban and battlefield surveillance applications. Technology advances developed under this project enable a single human operator to monitor activies over a broad area using a distributed network of active video sensors. The sensor platforms are mainly autonomous, notifying the operator only of salient information as it occurs, and engaging the operator minimally to alter platform operations.
A team composed of Carnegie Mellon University Robotics Institute and the Sarnoff Corporation were chosen to lead the technical efforts by developing an end-to-end testbed system demonstrating a wide range of advanced surveillance techniques: real-time moving object detection and tracking from stationary and moving camera platforms, recognition of generic object classes (e.g. human, sedan, truck) and specific object types (e.g. campus police car, FedEx van), object pose estimation with respect to a geospatial site model, active camera control and multi-camera cooperative tracking, human gait analysis, recognition of simple multi-agent activities, real-time data dissemination, data logging and dynamic scene visualization. Twelve other research contracts were awarded to university and industry labs to conduct research in focused technical areas that include human activity recognition, vehicle tracking and counting, airborne surveillance, novel sensor design, and geometric methods for graphical view transfer.
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© 2000 Carnegie Mellon University, Robotics Institute