Video Anomaly Detection Methods: a Survey
Video abnormal behavior detection is a hot research topic in computer vision. It involves extracting temporal and spatial features from video content to determine the presence of abnormal events and their types within the video, as well as to localize the regions and time where anomalies occur. This...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2024-12-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2404041.pdf |
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| author | WU Peichen, YUAN Lining, GUO Fang, LIU Zhao |
| author_facet | WU Peichen, YUAN Lining, GUO Fang, LIU Zhao |
| author_sort | WU Peichen, YUAN Lining, GUO Fang, LIU Zhao |
| collection | DOAJ |
| description | Video abnormal behavior detection is a hot research topic in computer vision. It involves extracting temporal and spatial features from video content to determine the presence of abnormal events and their types within the video, as well as to localize the regions and time where anomalies occur. This paper systematically reviews and categorizes existing methods for video abnormal behavior detection based on supervised/unsupervised learning. This paper categorizes the supervised methods into methods based on deviation mean calculation and multimodal methods. For unsupervised methods, it summarizes various completely unsupervised approaches. Starting from the current mainstream modeling approaches, this paper gives a detailed explanation of deviation mean calculation methods, summarizes multimodal methods based on the utilization and processing of different modal features, and introduces completely unsupervised methods based on two training approaches. By comparing the network architectures of different models, this paper summarizes the test datasets, use cases, advantages, and limitations of various abnormal behavior detection models. Furthermore, it compares and evaluates models using benchmark datasets and common evaluation standards such as frame-level and pixel-level standards, and conducts intra-class comparisons based on performance results, followed by analysis of the outcomes. Lastly, it explores trends in video abnormal behavior detection through five directions: virtual synthetic datasets, multimodal large models, lightweight models, etc. |
| format | Article |
| id | doaj-art-2b330547cfb945d097b8eb4bb14d7fb1 |
| institution | DOAJ |
| issn | 1673-9418 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj-art-2b330547cfb945d097b8eb4bb14d7fb12025-08-20T02:51:27ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182024-12-0118123100312510.3778/j.issn.1673-9418.2404041Video Anomaly Detection Methods: a SurveyWU Peichen, YUAN Lining, GUO Fang, LIU Zhao01. School of Information Network Security, People??s Public Security University of China, Beijing 100038, China 2. School of Public Security Big Data Modern Industry, Guangxi Police College, Nanning 530028, China 3. School of National Security, People??s Public Security University of China, Beijing 100038, China 4. Collaborative Innovation Center for Network Security and Rule of Law, People??s Public Security University of China, Beijing 100038, ChinaVideo abnormal behavior detection is a hot research topic in computer vision. It involves extracting temporal and spatial features from video content to determine the presence of abnormal events and their types within the video, as well as to localize the regions and time where anomalies occur. This paper systematically reviews and categorizes existing methods for video abnormal behavior detection based on supervised/unsupervised learning. This paper categorizes the supervised methods into methods based on deviation mean calculation and multimodal methods. For unsupervised methods, it summarizes various completely unsupervised approaches. Starting from the current mainstream modeling approaches, this paper gives a detailed explanation of deviation mean calculation methods, summarizes multimodal methods based on the utilization and processing of different modal features, and introduces completely unsupervised methods based on two training approaches. By comparing the network architectures of different models, this paper summarizes the test datasets, use cases, advantages, and limitations of various abnormal behavior detection models. Furthermore, it compares and evaluates models using benchmark datasets and common evaluation standards such as frame-level and pixel-level standards, and conducts intra-class comparisons based on performance results, followed by analysis of the outcomes. Lastly, it explores trends in video abnormal behavior detection through five directions: virtual synthetic datasets, multimodal large models, lightweight models, etc.http://fcst.ceaj.org/fileup/1673-9418/PDF/2404041.pdfabnormal behavior detection; deep learning; completely unsupervised; multimodal features |
| spellingShingle | WU Peichen, YUAN Lining, GUO Fang, LIU Zhao Video Anomaly Detection Methods: a Survey Jisuanji kexue yu tansuo abnormal behavior detection; deep learning; completely unsupervised; multimodal features |
| title | Video Anomaly Detection Methods: a Survey |
| title_full | Video Anomaly Detection Methods: a Survey |
| title_fullStr | Video Anomaly Detection Methods: a Survey |
| title_full_unstemmed | Video Anomaly Detection Methods: a Survey |
| title_short | Video Anomaly Detection Methods: a Survey |
| title_sort | video anomaly detection methods a survey |
| topic | abnormal behavior detection; deep learning; completely unsupervised; multimodal features |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2404041.pdf |
| work_keys_str_mv | AT wupeichenyuanliningguofangliuzhao videoanomalydetectionmethodsasurvey |