Security surveillance of public scene is closely relevant to routine safety of individual. Under the stimulus of this concern, abnormal event detection is becoming one of the most important tasks in computer vision and video processing. In this paper, we propose a new algorithm to address the visual abnormal detection problem. Our algorithm decouples the problem into a feature descriptor extraction process, followed by an AutoEncoder based network called cascade deep AutoEncoder (CDA). The movement information is represented by a novel descriptor capturing the multi-frame optical flow information. And then, the feature descriptor of the normal samples is fed into the CDA network for training. Finally, the abnormal samples are distinguished by the reconstruction error of the CDA in the testing procedure. We validate the proposed method on several video surveillance datasets.

中文翻译：

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通过多帧光流信息分析异常事件检测

公共场所的安全监控与个人的日常安全密切相关。在这种担忧的刺激下，异常事件检测已成为计算机视觉和视频处理中最重要的任务之一。在本文中，我们提出了一种解决视觉异常检测问题的新算法。我们的算法将问题分解成一个特征描述符提取过程，然后是一个基于自动编码器的网络，称为级联深度自动编码器（CDA）。运动信息由捕获多帧光流信息的新颖描述符表示。然后，将正常样本的特征描述符输入到CDA网络中进行训练。最后，在测试过程中通过CDA的重建误差来区分异常样品。