An lnternational lnterdisciplinary journal
Yoo-Joo Choi, Doo-Cheol Kang, We-Duke Cho, and Ku-Jin Kim
VOL. 14. NO. 7, 2523-2535, 2011-09

Situation awareness is the key to security. Awareness requires information that spans multiple scales of space and time. A security analyst needs to keep track of “who are the people and vehicles in aspace?” (identity tracking), “where are the people in a space?” (location tracking), and “what are the people/vehicles/objects in a space doing?” (activity tracking). The analyst also needs to use historical context to interpret this data. For example, the fact that the paper delivery truck showed up at 6 a.m. instead of the usual 8 a.m. would alert a security analyst. Smart video surveillance systems are capable of enhancing situational awareness across multiple scales of space and time. However, at the present time, the component technologies are evolving in isolation; for example, face recognition technology addresses the identity tracking challenge while constraining the subject to be in front of the camera, and intelligent video surveillance technologies provide activity detection capabilities on video streams while ignoring the identity tracking challenge. To provide comprehensive, nonintrusive situation awareness, it is imperative to address the challenge of multiscale, spatiotemporal tracking. This article explores the concepts of multiscale spatiotemporal tracking through the use of real-time video analysis, active cameras, multiple object models, and long-term pattern analysis to provide comprehensive situation awareness.
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