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

  • Kayleigh Boekhoudt (Creator)
  • Alina Matei (Creator)
  • Maya Aghaei (Creator)
  • Estefanía Talavera Martínez (Creator)



    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.
    Datum van beschikbaarheid5-aug-2021
    UitgeverUniversity of Groningen

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