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arXiv · 2108.00246

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

Kayleigh Boekhoudt·Alina Matei·Maya Aghaei·Estefanía Talavera
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Abstract

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.

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Kayleigh Boekhoudt, Alina Matei, Maya Aghaei, Estefanía Talavera. 2021-07-31. HR-Crime: Human-Related Anomaly Detection in Surveillance Videos. https://doi.org/10.34894/irrdje

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