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Algorithms for cooperative multisensor surveillance
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DOI：10.1109/5.959341.png)
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The Video Surveillance and Monitoring (VSAM) team at Carnegie Mellon University (CMU) has developed an end-to-end, multicamera surveillance system that allows, a single human operator to monitor activities in a cluttered environment using a distributed network of active video sensors. Video understanding algorithms have been developed to automatically detect people and vehicles, seamlessly track them using a network of cooperating active sensors, determine their three-dimensional locations with respect to a geospatial site model, and present this, information to a human operator who controls the system through a graphical user interface. The goal is to automatically collect and disseminate real-time information to improve the situational awareness of security providers and decision makers. The feasibility of real-time video surveillance has been demonstrated within a multicamera testbed system developed on the campus of CMU. This paper presents an overview of the issues and algorithms involved in creating this semiautonomous,, multicamera surveillance system.
Keyword:
active vision
cooperative systems
geolocation
multisensor systems
site security monitoring
user interfaces
video surveillance
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