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

DOI:10.34894/IRRDJE
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DOI: 10.34894/IRRDJE

Datacite citation style

Boekhoudt, Kayleigh; Matei, Alina; Aghaei, Maya; Estefanía Talavera Martínez (2021): HR-Crime: Human-Related Anomaly Detection in Surveillance Videos. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.34894/IRRDJE
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

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.

History

  • 2021-05-08 first online, published, posted

Publisher

4TU.ResearchData

Organizations

University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS)
University of Groningen, Computer Science
NHL Stenden University of Applied Sciences

DATA

Files (1)

  • 3,538,515,428 bytesMD5:f9f74bab5aef008a79dd7d89dae35ac7HR-Crime.zip