@article{(Open Science Index):https://publications.waset.org/pdf/2415,
	  title     = {ADABeV: Automatic Detection of Abnormal Behavior in Video-surveillance},
	  author    = {Nour Charara and  Iman Jarkass and  Maria Sokhn and  Elena Mugellini and  Omar Abou Khaled},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {8},
	  year      = {2012},
	  pages     = {946 - 952},
	  ee        = {https://publications.waset.org/pdf/2415},
	  url   	= {https://publications.waset.org/vol/68},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 68, 2012},