论文标题
智能监视的视频异常检测
Video Anomaly Detection for Smart Surveillance
论文作者
论文摘要
在现代智能视频监视系统中，通过计算机视觉分析的自动异常检测起着关键作用，不仅可以显着提高监视效率，而且减轻了实时监控的负担。视频中的异常被广泛定义为不寻常的事件或活动，表示不规则行为。异常检测的目的是在视频序列中时间或空间将异常事件定位。时间定位（即指示视频中异常事件的开始和终点）称为帧级检测。空间定位更具挑战性，意味着识别与异常事件相对应的每个异常框架内的像素。此设置通常称为像素级检测。在本文中，我们简要概述了视频异常检测的最新研究进展，并突出了一些未来的研究方向。
In modern intelligent video surveillance systems, automatic anomaly detection through computer vision analytics plays a pivotal role which not only significantly increases monitoring efficiency but also reduces the burden on live monitoring. Anomalies in videos are broadly defined as events or activities that are unusual and signify irregular behavior. The goal of anomaly detection is to temporally or spatially localize the anomaly events in video sequences. Temporal localization (i.e. indicating the start and end frames of the anomaly event in a video) is referred to as frame-level detection. Spatial localization, which is more challenging, means to identify the pixels within each anomaly frame that correspond to the anomaly event. This setting is usually referred to as pixel-level detection. In this paper, we provide a brief overview of the recent research progress on video anomaly detection and highlight a few future research directions.
