{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T19:24:00Z","timestamp":1777663440577,"version":"3.51.4"},"reference-count":86,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center","doi-asserted-by":"publisher","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center","doi-asserted-by":"publisher","award":["2023R1A2C1006944"],"award-info":[{"award-number":["2023R1A2C1006944"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}]},{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education","award":["2023R1A2C1006944"],"award-info":[{"award-number":["2023R1A2C1006944"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the recent rise in violent crime, the real-time situation analysis capabilities of the prevalent closed-circuit television have been employed for the deterrence and resolution of criminal activities. Anomaly detection can identify abnormal instances such as violence within the patterns of a specified dataset; however, it faces challenges in that the dataset for abnormal situations is smaller than that for normal situations. Herein, using datasets such as UBI-Fights, RWF-2000, and UCSD Ped1 and Ped2, anomaly detection was approached as a binary classification problem. Frames extracted from each video with annotation were reconstructed into a limited number of images of 3\u00d73, 4\u00d73, 4\u00d74, 5\u00d73 sizes using the method proposed in this paper, forming an input data structure similar to a light field and patch of vision transformer. The model was constructed by applying a convolutional block attention module that included channel and spatial attention modules to a residual neural network with depths of 10, 18, 34, and 50 in the form of a three-dimensional convolution. The proposed model performed better than existing models in detecting abnormal behavior such as violent acts in videos. For instance, with the undersampled UBI-Fights dataset, our network achieved an accuracy of 0.9933, a loss value of 0.0010, an area under the curve of 0.9973, and an equal error rate of 0.0027. These results may contribute significantly to solve real-world issues such as the detection of violent behavior in artificial intelligence systems using computer vision and real-time video monitoring.<\/jats:p>","DOI":"10.3390\/s23239616","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T12:15:14Z","timestamp":1701692114000},"page":"9616","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Anomaly Detection Based on a 3D Convolutional Neural Network Combining Convolutional Block Attention Module Using Merged Frames"],"prefix":"10.3390","volume":"23","author":[{"given":"In-Chang","family":"Hwang","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4333-2852","authenticated-orcid":false,"given":"Hyun-Soo","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"ref_1","first-page":"139","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. 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