论文标题
评估：可解释的视频异常本地化
EVAL: Explainable Video Anomaly Localization
论文作者
论文摘要
我们为单场视频异常本地化开发了一个新颖的框架，该框架允许为系统做出的决策而理解的人为理解的原因。我们首先学习对象及其动作的一般表示（使用深网），然后使用这些表示形式来构建任何特定场景的高级，位置依赖的模型。该模型可用于检测同一场景的新视频中的异常情况。重要的是，我们的方法是可以解释的 - 我们的高级外观和运动特征可以为为什么视频的任何部分被归类为正常或异常的原因提供了可理解的理由。我们在标准视频异常检测数据集（街头场景，Cuhk Avenue，Shanghaitech和UCSD PED1，PED2）上进行实验，并比以前的最新面前表现出显着改善。
We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent model of any particular scene. This model can be used to detect anomalies in new videos of the same scene. Importantly, our approach is explainable - our high-level appearance and motion features can provide human-understandable reasons for why any part of a video is classified as normal or anomalous. We conduct experiments on standard video anomaly detection datasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show significant improvements over the previous state-of-the-art.
