@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}, }