ADABeV: Automatic Detection of Abnormal Behavior in Video-surveillance

Autor: Nour Charara, Iman Jarkass, Maria Sokhn, Elena Mugellini, Omar ABOU KHALED
Jazyk: angličtina
Rok vydání: 2012
Předmět:
Zdroj: BASE-Bielefeld Academic Search Engine
DOI: 10.5281/zenodo.1057171
Popis: 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.
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Databáze: OpenAIRE
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