Region Based Anomaly Detection with Real-Time Training and Analysis
Region Based Anomaly Detection with Real-Time Training and Analysis
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DOI：
10.1109/icmla.2019.00092
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Philip A. Adey;Oliver K. Hamilton;M. Bordewich;T. Breckon
中科院分区：
文献类型：
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作者：
Philip A. Adey;Oliver K. Hamilton;M. Bordewich;T. Breckon
我们提出了一种能够对实时图像流进行实时操作的异常检测方法。实时性能适用于算法的训练以及后续分析，并且是通过用使整体方法更高效的机制替代[9]中使用的区域提议机制来实现的。他们为每个图像生成数千个区域，而我们生成的区域要少得多，但目标更好。我们还提出了一种“卷积”变体，它完全消除了区域提取，并对两种变体中使用的密度估计阶段提出了改进。
We present a method of anomaly detection that is capable of real-time operation on a live stream of images. The real-time performance applies to the training of the algorithm as well as subsequent analysis, and is achieved by substituting the region proposal mechanism used in [9] with one that makes the overall method more efficient. where they generate thousands of regions per image, we generate far fewer but better targeted regions. We also propose a 'convolutional' variant which does away with region extraction altogether, and propose improvements to the density estimation phase used in both variants.