计算机科学
事件(粒子物理)
假警报
人工智能
基本事实
集合(抽象数据类型)
软件部署
比例(比率)
警报
算法
计算机视觉
模式识别(心理学)
数据挖掘
实时计算
物理
材料科学
量子力学
复合材料
程序设计语言
操作系统
作者
A. Adam,Ehud Rivlin,Ilan Shimshoni,Daviv Reinitz
标识
DOI:10.1109/tpami.2007.70825
摘要
We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual, and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficient low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in realtime. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.
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