ADABeV: Automatic Detection of Abnormal Behavior in Video-surveillance
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | |
| Jahr: | 2012 |
| Monat: | August |
| Ort: | Oslo, Norway |
| Organisation: | International Conference on Image, Signal and Vision Computing |
| Abriss: | 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. |
| Schlagworte: | Behavior recognition, Crowded scene, data fusion, Pattern Recognition, Video-surveillance |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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