Anomaly detection in video sequences: A benchmark and computational model

Abstract Anomaly detection has attracted considerable search attention. However, existing anomaly detection databases encounter two major problems. Firstly, they are limited in scale. Secondly, training sets contain only video‐level labels indicating the existence of an abnormal event during the ful...

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Autores principales:	Boyang Wan, Wenhui Jiang, Yuming Fang, Zhiyuan Luo, Guanqun Ding
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Photography TR1-1050 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/c060f34ff9c543b384db6115e37fb8a6
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spelling	oai:doaj.org-article:c060f34ff9c543b384db6115e37fb8a62021-11-29T03:38:16ZAnomaly detection in video sequences: A benchmark and computational model1751-96671751-965910.1049/ipr2.12258https://doaj.org/article/c060f34ff9c543b384db6115e37fb8a62021-12-01T00:00:00Zhttps://doi.org/10.1049/ipr2.12258https://doaj.org/toc/1751-9659https://doaj.org/toc/1751-9667Abstract Anomaly detection has attracted considerable search attention. However, existing anomaly detection databases encounter two major problems. Firstly, they are limited in scale. Secondly, training sets contain only video‐level labels indicating the existence of an abnormal event during the full video while lacking annotations of precise time durations. To tackle these problems, we contribute a new Large‐scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences, which is featured in two aspects. 1) It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence etc. with large scene varieties, making it the largest anomaly analysis database to date. 2) It provides the annotation data, including video‐level labels (abnormal/normal video, anomaly type) and frame‐level labels (abnormal/normal video frame) to facilitate anomaly detection. Leveraging the above benefits from the LAD database, we further formulate anomaly detection as a fully supervised learning problem and propose a multi‐task deep neural network to solve it. We firstly obtain the local spatiotemporal contextual feature by using an Inflated 3D convolutional (I3D) network. Then we construct a recurrent convolutional neural network fed the local spatiotemporal contextual feature to extract the spatiotemporal contextual feature. With the global spatiotemporal contextual feature, the anomaly type and score can be computed simultaneously by a multi‐task neural network. Experimental results show that the proposed method outperforms the state‐of‐the‐art anomaly detection methods on our database and other public databases of anomaly detection. Supplementary materials are available at http://sim.jxufe.cn/JDMKL/ymfang/anomaly‐detection.html.Boyang WanWenhui JiangYuming FangZhiyuan LuoGuanqun DingWileyarticlePhotographyTR1-1050Computer softwareQA76.75-76.765ENIET Image Processing, Vol 15, Iss 14, Pp 3454-3465 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photography TR1-1050 Computer software QA76.75-76.765
spellingShingle	Photography TR1-1050 Computer software QA76.75-76.765 Boyang Wan Wenhui Jiang Yuming Fang Zhiyuan Luo Guanqun Ding Anomaly detection in video sequences: A benchmark and computational model
description	Abstract Anomaly detection has attracted considerable search attention. However, existing anomaly detection databases encounter two major problems. Firstly, they are limited in scale. Secondly, training sets contain only video‐level labels indicating the existence of an abnormal event during the full video while lacking annotations of precise time durations. To tackle these problems, we contribute a new Large‐scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences, which is featured in two aspects. 1) It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence etc. with large scene varieties, making it the largest anomaly analysis database to date. 2) It provides the annotation data, including video‐level labels (abnormal/normal video, anomaly type) and frame‐level labels (abnormal/normal video frame) to facilitate anomaly detection. Leveraging the above benefits from the LAD database, we further formulate anomaly detection as a fully supervised learning problem and propose a multi‐task deep neural network to solve it. We firstly obtain the local spatiotemporal contextual feature by using an Inflated 3D convolutional (I3D) network. Then we construct a recurrent convolutional neural network fed the local spatiotemporal contextual feature to extract the spatiotemporal contextual feature. With the global spatiotemporal contextual feature, the anomaly type and score can be computed simultaneously by a multi‐task neural network. Experimental results show that the proposed method outperforms the state‐of‐the‐art anomaly detection methods on our database and other public databases of anomaly detection. Supplementary materials are available at http://sim.jxufe.cn/JDMKL/ymfang/anomaly‐detection.html.
format	article
author	Boyang Wan Wenhui Jiang Yuming Fang Zhiyuan Luo Guanqun Ding
author_facet	Boyang Wan Wenhui Jiang Yuming Fang Zhiyuan Luo Guanqun Ding
author_sort	Boyang Wan
title	Anomaly detection in video sequences: A benchmark and computational model
title_short	Anomaly detection in video sequences: A benchmark and computational model
title_full	Anomaly detection in video sequences: A benchmark and computational model
title_fullStr	Anomaly detection in video sequences: A benchmark and computational model
title_full_unstemmed	Anomaly detection in video sequences: A benchmark and computational model
title_sort	anomaly detection in video sequences: a benchmark and computational model
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/c060f34ff9c543b384db6115e37fb8a6
work_keys_str_mv	AT boyangwan anomalydetectioninvideosequencesabenchmarkandcomputationalmodel AT wenhuijiang anomalydetectioninvideosequencesabenchmarkandcomputationalmodel AT yumingfang anomalydetectioninvideosequencesabenchmarkandcomputationalmodel AT zhiyuanluo anomalydetectioninvideosequencesabenchmarkandcomputationalmodel AT guanqunding anomalydetectioninvideosequencesabenchmarkandcomputationalmodel
_version_	1718407670993518592

Anomaly detection in video sequences: A benchmark and computational model

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