HR-Crime: Human-Related Anomaly Detection in Surveillance Videos

The automatic detection of anomalies captured by surveillance settings is essential for speeding the otherwise laborious approach. To date, UCF-Crime is the largest available dataset for automatic visual analysis of anomalies and consists of real-world crime scenes of various categories. In this paper, we introduce HR-Crime, a subset of the UCF-Crime dataset suitable for human-related anomaly detection tasks. We rely on state-of-the-art techniques to build the feature extraction pipeline for human-related anomaly detection. Furthermore, we present the baseline anomaly detection analysis on the HR-Crime. HR-Crime as well as the developed feature extraction pipeline and the extracted features will be publicly available for further research in the field.对监控场景下捕获的异常行为进行自动检测，对于大幅简化原本耗时费力的检测流程至关重要。截至目前，UCF-Crime是现有规模最大的异常行为视觉自动分析数据集，涵盖多类真实犯罪场景。本文提出了HR-Crime数据集——UCF-Crime的一个子集，适用于与人类行为相关的异常检测任务。我们采用当前最先进的技术，构建了面向人类相关异常检测的特征提取流水线。此外，我们还基于HR-Crime数据集开展了异常检测基准分析。HR-Crime数据集、所开发的特征提取流水线以及提取得到的特征，都将对该领域的后续研究公开共享。

搜集汇总

背景与挑战

背景概述

HR-Crime是UCF-Crime数据集的专门子集，专注于监控视频中人类相关异常检测任务，旨在加速自动视觉分析。该数据集包含一个特征提取管道和预提取特征，支持进一步研究，并基于真实犯罪场景构建，适用于人工智能和图像处理领域。