Behavior recognition architecture for surveillance applications

Differentiating between normal human activity and aberrant behavior via closed circuit television cameras is a difficult and fatiguing task. The vigilance required of human observers when engaged in such tasks must remain constant, yet attention falls off dramatically over time. In this paper we propose an architecture for capturing data and creating a test and evaluation system to monitor video sensors and tag aberrant human activities for immediate review by human monitors. A psychological perspective provides the inspiration of depicting isolated human motion by point-light walker (PLW) displays, as they have been shown to be salient for recognition of action. Low level intent detection features are used to provide an initial evaluation of actionable behaviors. This relies on strong tracking algorithms that can function in an unstructured environment under a variety of environmental conditions. Critical to this is creating a description of ldquosuspicious behaviorrdquo that can be used by the automated system. The resulting confidence value assessments are useful for monitoring human activities and could potentially provide early warning of IED placement activities.