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.

对监控场景下采集到的异常事件进行自动检测，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自动视觉分析数据集，涵盖多类真实犯罪场景。本文提出了HR-Crime，它是UCF-Crime的一个子集，适用于与人类相关的异常检测任务。我们依托当前最先进的技术，搭建了面向人类相关异常检测的特征提取流水线。此外，我们还在HR-Crime上开展了基准异常检测分析。HR-Crime、所搭建的特征提取流水线以及提取得到的特征都将向公众开放，以供该领域的后续研究使用。