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

Kayleigh Boekhoudt*, Alina Matei, Maya Aghaei, Estefanía Talavera

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
290 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings
EditorsNicolas Tsapatsoulis, Andreas Panayides, Theo Theocharides, Andreas Lanitis, Andreas Lanitis, Constantinos Pattichis, Constantinos Pattichis, Mario Vento
Number of pages11
ISBN (Print)9783030891305
Publication statusPublished - 31 Oct 2021
Event19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021 - Virtual, Online
Duration: 28 Sept 202130 Sept 2021
Conference number: 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13053 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021
Abbreviated titleCAIP 2021
CityVirtual, Online


  • Forensics
  • Human-related anomaly detection
  • Surveillance videos


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