%0 Journal Article
	%A Nour Charara and  Iman Jarkass and  Maria Sokhn and  Elena Mugellini and  Omar Abou Khaled
	%D 2012
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 68, 2012
	%T ADABeV: Automatic Detection of Abnormal Behavior in Video-surveillance
	%U https://publications.waset.org/pdf/2415
	%V 68
	%X Intelligent Video-Surveillance (IVS) systems are
being more and more popular in security applications. The analysis
and recognition of abnormal behaviours in a video sequence has
gradually drawn the attention in the field of IVS, since it allows
filtering out a large number of useless information, which guarantees
the high efficiency in the security protection, and save a lot of human
and material resources. We present in this paper ADABeV, an
intelligent video-surveillance framework for event recognition in
crowded scene to detect the abnormal human behaviour. This
framework is attended to be able to achieve real-time alarming,
reducing the lags in traditional monitoring systems. This architecture
proposal addresses four main challenges: behaviour understanding in
crowded scenes, hard lighting conditions, multiple input kinds of
sensors and contextual-based adaptability to recognize the active
context of the scene.
	%P 946 - 952