Behavior understanding and semantic interpretation of dynamic visual scenes have attracted a lot of attention in computer vision research community. Although the use of surveillance cameras has proliferated, the understanding of activities still remains complex. While users are mostly interested in high level and subjective semantics, only low level visual features can be extracted in a reliable way. This paper presents a novel framework for video guided behavior monitoring, built around the event modeling concept. It enables users to design their personal models of events combining elementary concept and low level features using expressive formalisms. The framework enables then detection of the events within video streams based on low level features extraction and manual annotations analysis, while taking in consideration uncertainty. Examples depicting content-based events modeling and detection from video surveillance are presented to illustrate the approach.