Semi-supervised anomaly detection in video-surveillance scenes in the wild

Surveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. Hence, manual monitoring of such anomalies may be exhaustive and monotonous, resulting in a d...

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Detalles Bibliográficos
Autores: Sarker, Mohammad Ibrahim, Losada Gutiérrez, Cristina|||0000-0001-9545-327X, Marrón Romera, Marta|||0000-0001-7723-2262, Fuentes Jiménez, David|||0000-0001-6424-4782, Luengo Sánchez, Sara|||0000-0003-3942-3804
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/58360
Acceso en línea:http://hdl.handle.net/10017/58360
https://dx.doi.org/10.3390/s21123993
Access Level:acceso abierto
Palabra clave:Anomaly detection
RGB
CNN
Multiple instance learning
Video-surveillance
Electrónica
Electronics
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repository_id_str
spelling Semi-supervised anomaly detection in video-surveillance scenes in the wildSarker, Mohammad IbrahimLosada Gutiérrez, Cristina|||0000-0001-9545-327XMarrón Romera, Marta|||0000-0001-7723-2262Fuentes Jiménez, David|||0000-0001-6424-4782Luengo Sánchez, Sara|||0000-0003-3942-3804Anomaly detectionRGBCNNMultiple instance learningVideo-surveillanceElectrónicaElectronicsSurveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. Hence, manual monitoring of such anomalies may be exhaustive and monotonous, resulting in a decrease in reliability and speed in emergency situations due to monitor tiredness. Within this framework, the importance of automatic detection of anomalies is clear, and, therefore, an important amount of research works have been made lately in this topic. According to these earlier studies, supervised approaches perform better than unsupervised ones. However, supervised approaches demand manual annotation, making dependent the system reliability of the different situations used in the training (something difficult to set in anomaly context). In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm. Spatio-temporal features are extracted from each surveillance video using a temporal convolutional 3D neural network (T-C3D). Then, a novel ranking loss function increases the distance between the classification scores of anomalous and normal videos, reducing the number of false negatives. The proposal has been evaluated and compared against state-of-art approaches, obtaining competitive performance without fine-tuning, which also validates its generalization capability. In this paper, the proposal design and reliability is presented and analyzed, as well as the aforementioned quantitative and qualitative evaluation in-the-wild scenarios, demonstrating its high sensitivity in anomaly detection in all of them.info:eu-repo/grantAgreement/UAH//CCG2020%2FIA-043Agencia Estatal de InvestigaciónMinisterio de Economía y CompetitividadUniversidad de AlcaláMDPI20212021-06-04journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/58360https://dx.doi.org/10.3390/s21123993reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 Not available TIN2016-75982-C2-1-R DETECCION SEMANTICA MULTISENSORIAL DE SITUACIONES ANOMALAS EN ENTORNOS SIN RESTRICCIONESAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2016-80939-R RECONSTRUCCION DE OBJETOS DEFORMABLES A PARTIR DE IMAGENES Y SUS APLICACIONES A LA REALIDAD AUMENTADA EN CIRUGIA MINIMAMENTE INVASIVAUAH Not available CCG2020%2FIA-043UAH Not available CCG2019%2FIA-024open accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/583602026-06-18T11:13:07Z
dc.title.none.fl_str_mv Semi-supervised anomaly detection in video-surveillance scenes in the wild
title Semi-supervised anomaly detection in video-surveillance scenes in the wild
spellingShingle Semi-supervised anomaly detection in video-surveillance scenes in the wild
Sarker, Mohammad Ibrahim
Anomaly detection
RGB
CNN
Multiple instance learning
Video-surveillance
Electrónica
Electronics
title_short Semi-supervised anomaly detection in video-surveillance scenes in the wild
title_full Semi-supervised anomaly detection in video-surveillance scenes in the wild
title_fullStr Semi-supervised anomaly detection in video-surveillance scenes in the wild
title_full_unstemmed Semi-supervised anomaly detection in video-surveillance scenes in the wild
title_sort Semi-supervised anomaly detection in video-surveillance scenes in the wild
dc.creator.none.fl_str_mv Sarker, Mohammad Ibrahim
Losada Gutiérrez, Cristina|||0000-0001-9545-327X
Marrón Romera, Marta|||0000-0001-7723-2262
Fuentes Jiménez, David|||0000-0001-6424-4782
Luengo Sánchez, Sara|||0000-0003-3942-3804
author Sarker, Mohammad Ibrahim
author_facet Sarker, Mohammad Ibrahim
Losada Gutiérrez, Cristina|||0000-0001-9545-327X
Marrón Romera, Marta|||0000-0001-7723-2262
Fuentes Jiménez, David|||0000-0001-6424-4782
Luengo Sánchez, Sara|||0000-0003-3942-3804
author_role author
author2 Losada Gutiérrez, Cristina|||0000-0001-9545-327X
Marrón Romera, Marta|||0000-0001-7723-2262
Fuentes Jiménez, David|||0000-0001-6424-4782
Luengo Sánchez, Sara|||0000-0003-3942-3804
author2_role author
author
author
author
dc.subject.none.fl_str_mv Anomaly detection
RGB
CNN
Multiple instance learning
Video-surveillance
Electrónica
Electronics
topic Anomaly detection
RGB
CNN
Multiple instance learning
Video-surveillance
Electrónica
Electronics
description Surveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. Hence, manual monitoring of such anomalies may be exhaustive and monotonous, resulting in a decrease in reliability and speed in emergency situations due to monitor tiredness. Within this framework, the importance of automatic detection of anomalies is clear, and, therefore, an important amount of research works have been made lately in this topic. According to these earlier studies, supervised approaches perform better than unsupervised ones. However, supervised approaches demand manual annotation, making dependent the system reliability of the different situations used in the training (something difficult to set in anomaly context). In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm. Spatio-temporal features are extracted from each surveillance video using a temporal convolutional 3D neural network (T-C3D). Then, a novel ranking loss function increases the distance between the classification scores of anomalous and normal videos, reducing the number of false negatives. The proposal has been evaluated and compared against state-of-art approaches, obtaining competitive performance without fine-tuning, which also validates its generalization capability. In this paper, the proposal design and reliability is presented and analyzed, as well as the aforementioned quantitative and qualitative evaluation in-the-wild scenarios, demonstrating its high sensitivity in anomaly detection in all of them.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-06-04
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/58360
https://dx.doi.org/10.3390/s21123993
url http://hdl.handle.net/10017/58360
https://dx.doi.org/10.3390/s21123993
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 Not available TIN2016-75982-C2-1-R DETECCION SEMANTICA MULTISENSORIAL DE SITUACIONES ANOMALAS EN ENTORNOS SIN RESTRICCIONES
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2016-80939-R RECONSTRUCCION DE OBJETOS DEFORMABLES A PARTIR DE IMAGENES Y SUS APLICACIONES A LA REALIDAD AUMENTADA EN CIRUGIA MINIMAMENTE INVASIVA
UAH Not available CCG2020%2FIA-043
UAH Not available CCG2019%2FIA-024
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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