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.

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。