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The automatic detection of anomalies captured by surveillance settings isessential for speeding the otherwise laborious approach. To date, UCF-Crime isthe largest available dataset for automatic visual analysis of anomalies andconsists of real-world crime scenes of various categories. In this paper, weintroduce HR-Crime, a subset of the UCF-Crime dataset suitable forhuman-related anomaly detection tasks. We rely on state-of-the-art techniquesto build the feature extraction pipeline for human-related anomaly detection.Furthermore, we present the baseline anomaly detection analysis on theHR-Crime. HR-Crime as well as the developed feature extraction pipeline and theextracted features will be publicly available for further research in thefield.

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