Abstract
Aerial Video-Surveillance systems are being more and more used in security applications. The analysis and detection of abnormal behaviours in a aerial sequence has progressively drawn the attention in the field of public area security, 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 an intelligent video-surveillance framework for abnormal behaviour detection in aerial video surveillance. This framework is attended to be able to achieve real-time alarming, in public areas. This architecture takes into consideration four main challenges: behaviour understanding in public area, aerial video challenges, unstable video and contextual-based adaptability to recognize the active context of the scene.
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