{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:59:12Z","timestamp":1774951152394,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Engineering and Physical Sciences Research Council","award":["EP\/M026981\/1"],"award-info":[{"award-number":["EP\/M026981\/1"]}]},{"name":"Engineering and Physical Sciences Research Council","award":["EP\/T021063\/1"],"award-info":[{"award-number":["EP\/T021063\/1"]}]},{"name":"Engineering and Physical Sciences Research Council","award":["EP\/T024917\/"],"award-info":[{"award-number":["EP\/T024917\/"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Reconstruction-based and prediction-based approaches are widely used for video anomaly detection (VAD) in smart city surveillance applications. However, neither of these approaches can effectively utilize the rich contextual information that exists in videos, which makes it difficult to accurately perceive anomalous activities. In this paper, we exploit the idea of a training model based on the \u201cCloze Test\u201d strategy in natural language processing (NLP) and introduce a novel unsupervised learning framework to encode both motion and appearance information at an object level. Specifically, to store the normal modes of video activity reconstructions, we first design an optical stream memory network with skip connections. Secondly, we build a space\u2013time cube (STC) for use as the basic processing unit of the model and erase a patch in the STC to form the frame to be reconstructed. This enables a so-called \u201dincomplete event (IE)\u201d to be completed. On this basis, a conditional autoencoder is utilized to capture the high correspondence between optical flow and STC. The model predicts erased patches in IEs based on the context of the front and back frames. Finally, we employ a generating adversarial network (GAN)-based training method to improve the performance of VAD. By distinguishing the predicted erased optical flow and erased video frame, the anomaly detection results are shown to be more reliable with our proposed method which can help reconstruct the original video in IE. Comparative experiments conducted on the benchmark UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets demonstrate AUROC scores reaching 97.7%, 89.7%, and 75.8%, respectively.<\/jats:p>","DOI":"10.3390\/s23104828","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T07:03:58Z","timestamp":1684393438000},"page":"4828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction"],"prefix":"10.3390","volume":"23","author":[{"given":"Heqing","family":"Huang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5714-3756","authenticated-orcid":false,"given":"Bing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Beijing 100085, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1489-0812","authenticated-orcid":false,"given":"Fei","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100190, China"},{"name":"Beihang Hangzhou Innovation Institute Yuhang, Xixi Octagon City, Yuhang District, Hangzhou 310023, China"}]},{"given":"Penghui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100190, China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-082X","authenticated-orcid":false,"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[{"name":"Cyber and Big Data Research Laboratory, Edinburgh Napier University, Edinburgh EH11 4BN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jakob, P., Madan, M., Schmid-Schirling, T., and Valada, A. 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