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

Kayleigh Boekhoudt; Alina Matei; Maya Aghaei; Estefanía Talavera

doi:10.1007/978-3-030-89131-2_15

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 proceeding › Conference contribution › Academic › peer-review

10 Citations (Scopus)

473 Downloads (Pure)

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.

Original language	English
Title of host publication	Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings
Editors	Nicolas Tsapatsoulis, Andreas Panayides, Theo Theocharides, Andreas Lanitis, Andreas Lanitis, Constantinos Pattichis, Constantinos Pattichis, Mario Vento
Publisher	Springer
Pages	164-174
Number of pages	11
ISBN (Print)	9783030891305
DOIs	https://doi.org/10.1007/978-3-030-89131-2_15
Publication status	Published - 31 Oct 2021
Event	19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021 - Virtual, Online Duration: 28 Sept 2021 → 30 Sept 2021 Conference number: 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13053 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021
Abbreviated title	CAIP 2021
City	Virtual, Online
Period	28/09/21 → 30/09/21

Keywords

2022 OA procedure
Human-related anomaly detection
Surveillance videos
Forensics

Access to Document

10.1007/978-3-030-89131-2_15

10.1007_978-3-030-89131-2_15Final published version, 2.52 MBLicence: Taverne

Cite this

Boekhoudt, K., Matei, A., Aghaei, M., & Talavera, E. (2021). HR-Crime: Human-Related Anomaly Detection in Surveillance Videos. In N. Tsapatsoulis, A. Panayides, T. Theocharides, A. Lanitis, A. Lanitis, C. Pattichis, C. Pattichis, & M. Vento (Eds.), Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings (pp. 164-174). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13053 LNCS). Springer. https://doi.org/10.1007/978-3-030-89131-2_15

Boekhoudt, Kayleigh ; Matei, Alina ; Aghaei, Maya et al. / HR-Crime : Human-Related Anomaly Detection in Surveillance Videos. Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings. editor / Nicolas Tsapatsoulis ; Andreas Panayides ; Theo Theocharides ; Andreas Lanitis ; Andreas Lanitis ; Constantinos Pattichis ; Constantinos Pattichis ; Mario Vento. Springer, 2021. pp. 164-174 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "2022 OA procedure, Human-related anomaly detection, Surveillance videos, Forensics",

author = "Kayleigh Boekhoudt and Alina Matei and Maya Aghaei and Estefan{\'i}a Talavera",

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Boekhoudt, K, Matei, A, Aghaei, M & Talavera, E 2021, HR-Crime: Human-Related Anomaly Detection in Surveillance Videos. in N Tsapatsoulis, A Panayides, T Theocharides, A Lanitis, A Lanitis, C Pattichis, C Pattichis & M Vento (eds), Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13053 LNCS, Springer, pp. 164-174, 19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021, Virtual, Online, 28/09/21. https://doi.org/10.1007/978-3-030-89131-2_15

HR-Crime: Human-Related Anomaly Detection in Surveillance Videos. / Boekhoudt, Kayleigh; Matei, Alina; Aghaei, Maya et al.
Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings. ed. / Nicolas Tsapatsoulis; Andreas Panayides; Theo Theocharides; Andreas Lanitis; Andreas Lanitis; Constantinos Pattichis; Constantinos Pattichis; Mario Vento. Springer, 2021. p. 164-174 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13053 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Matei, Alina

AU - Aghaei, Maya

AU - Talavera, Estefanía

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AB - 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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PB - Springer

Y2 - 28 September 2021 through 30 September 2021

ER -

Boekhoudt K, Matei A, Aghaei M, Talavera E. HR-Crime: Human-Related Anomaly Detection in Surveillance Videos. In Tsapatsoulis N, Panayides A, Theocharides T, Lanitis A, Lanitis A, Pattichis C, Pattichis C, Vento M, editors, Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings. Springer. 2021. p. 164-174. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-89131-2_15

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

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