Recently, surveillance systems have been widely used to analyze video recordings captured by surveillance cameras and to detect abnormal or irregular events in real-world scenes. In this study, we present a novel system that detects abnormal events. Unlike conventional methods, we consider abnormal event detection as variation matching problems. In approaching this problem, we transform from a single video to multiple ones by imposing variations on the video. Using a fully connected cross-entropy Monte Carlo method, we match multiple videos in a fully connected manner and detect abnormal events in all the videos concurrently. The experimental results show that our method can accurately detect abnormal events in multiple videos. Our proposed method can be used to automatically recognize abnormal events included in multi-view CCTV videos, which are available at airport terminals and underground stations.

最近，监视系统已被广泛用于分析监视摄像机捕获的视频记录并检测现实场景中的异常或不规则事件。在这项研究中，我们提出了一种检测异常事件的新颖系统。与常规方法不同，我们将异常事件检测视为变异匹配问题。在解决此问题时，我们通过在视频上施加变体来将其从单个视频转换为多个视频。使用完全连接的交叉熵蒙特卡洛方法，我们以完全连接的方式匹配多个视频，并同时检测所有视频中的异常事件。实验结果表明，我们的方法可以准确地检测出多个视频中的异常事件。我们提出的方法可用于自动识别多视图CCTV视频中包含的异常事件，