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Discovery of Anomalous Event against Frequent Sequence of Video Events

机译：针对视频事件的频繁序列发现异常事件

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《International Conference on Computational Intelligence and Software Engineering;CiSE 2009》|2009年|1-5|共5页
会议地点 Wuhan(CN);Wuhan(CN)
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Anwar Fahad; Morris Tim;
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1. Mining anomalous events against frequent sequences in surveillance videos from commercial environments [J] . Fahad Anwar, Ilias Petrounias, Tim Morris, Expert Systems with Application . 2012,第4期

机译：针对来自商业环境的监视视频中的频繁序列挖掘异常事件
2. Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos [J] . Rensso Victor Hugo Mora Colque, Carlos Caetano, Matheus Toledo Lustosa de Andrade, Circuits and Systems for Video Technology, IEEE Transactions on . 2017,第3期

机译：光学流方向，幅值和熵的直方图，用于检测视频中的异常事件
3. Detecting anomalous events in videos by learning deep representations of appearance and motion [J] . Dan Xu, Yan Yan, Elisa Ricci, Computer vision and image understanding . 2017,第Mara期

机译：通过学习外观和运动的深刻表示来检测视频中的异常事件
4. Discovery of Anomalous event against frequent sequence of video events [C] . Fahad Anwar, Tim Morris International Conference on Computational Intelligence and Software Engineering . 2009

机译：发现反对频繁的视频事件序列的异常事件
5. Multi-object tracking, event modeling, and activity discovery in video sequences. [D] . Joo, Seong-Wook. 2007

机译：视频序列中的多对象跟踪，事件建模和活动发现。
6. High Quality Maize Centromere 10 Sequence Reveals Evidence of Frequent Recombination Events [O] . Thomas K. Wolfgruber, Megan M. Nakashima, Kevin L. Schneider, -1

机译：高质量玉米着丝粒10序列揭示了频繁重组事件的证据
7. An APRIORI-based Method for Frequent Composite Event Discovery in Videos [O] . Alexander Toshev, François Brémond, Monique Thonnat 2010

机译：基于APRIORI的视频中频繁复合事件发现方法
8. Re-Assessment of Road Accident Data-Analysis Policy: Applying Theory from Involuntary, High-Consequence, Low-Probability Events like Nuclear Power Plant Meltdowns to Voluntary, Low-Consequence, High-Probability Events like Traffic Accidents. [R] . Naveh, E., Marcus, A. 2002

机译：道路交通事故数据的重新评估 - 分析政策：将核电厂危机等高概率，低概率事件的理论应用于交通事故等自愿，低后果，高概率事件。

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