cff-version: 1.2.0
abstract: "<p>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.</p>"
authors:
  - family-names: Boekhoudt
    given-names: Kayleigh
  - family-names: Matei
    given-names: Alina
    orcid: "https://orcid.org/0000-0002-8169-6219"
  - family-names: Aghaei
    given-names: Maya
    orcid: "https://orcid.org/0000-0002-2648-5945"
  - family-names: Talavera Martínez
    given-names: Estefanía
    orcid: "https://orcid.org/0000-0001-5918-8990"
title: "HR-Crime: Human-Related Anomaly Detection in Surveillance Videos"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.34894/IRRDJE
license: CC BY-NC-ND 4.0
date-released: 2021-05-08