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              <p>Behavior understanding and semantic interpretationof dynamic visual scenes have attracted a lot of attentionin computer vision research community. Althoughthe use of surveillance cameras has proliferated, the understandingof activities still remains complex. Whileusers are mostly interested in high level and subjectivesemantics, only low level visual features can be extractedin a reliable way. This paper presents a novelframework for video guided behavior monitoring, builtaround the event modeling concept. It enables users todesign their personal models of events combining elementaryconcept and low level features using expressiveformalisms. The framework enables then detectionof the events within video streams based on lowlevel features extraction and manual annotations analysis,while taking in consideration uncertainty. Examplesdepicting content-based events modeling and detectionfrom video surveillance are presented to illustrate theapproach.</p>
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