A novel reconstruction-based video anomaly detection with idempotent generative network
Video anomaly detection (VAD) is vital in intelligent security for public safety. Reconstruction-based VAD has received increasing research attention, but faces challenges such as missing anomalies for the reconstruction error as a criterion, and information loss when suppressing anomalous data, exi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825004144 |
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| _version_ | 1849334919243759616 |
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| author | Wenmin Dong Lifeng Zhang Wenjuan Shi Xiangwei Zheng Yuang Zhang |
| author_facet | Wenmin Dong Lifeng Zhang Wenjuan Shi Xiangwei Zheng Yuang Zhang |
| author_sort | Wenmin Dong |
| collection | DOAJ |
| description | Video anomaly detection (VAD) is vital in intelligent security for public safety. Reconstruction-based VAD has received increasing research attention, but faces challenges such as missing anomalies for the reconstruction error as a criterion, and information loss when suppressing anomalous data, existing methods also struggle to detect unseen anomalies. We propose a novel reconstruction-based video anomaly detection with idempotent generative network (RVADIGN), which is composed of the novel reconstruction module namely PSVAE and an idempotent loss term (IGN). Specifically, video frames are reconstructed within PSVAE. During this process, skip connections are established between the encoder and decoder to enhance contextual understanding. Finite Scalar Quantization (FSQ) layer is designed to discretize the encoder’s output, preserving key discriminative features. Meanwhile, the Pyramid Deformation Module (PDM), as an integral part of PSVAE, computes offset maps of original video frames for anomaly detection supplementation. Alongside PSVAE, idempotence is introduced as a regularity term, which projects the anomaly information back to the estimated manifolds of the target distribution, improves the adaptability and stability of the reconstruction method in different videos. Extensive experimental results demonstrate that our method outperforms other state-of-the-art VAD methods, achieving 99.03%, 92.40%, and 77.20% AUC on UCSD Ped2, CUHK Avenue, and ShanghaiTech, respectively. |
| format | Article |
| id | doaj-art-2e02cc25adfe471fbb245a1ccd4e6d79 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-2e02cc25adfe471fbb245a1ccd4e6d792025-08-20T03:45:27ZengElsevierAlexandria Engineering Journal1110-01682025-06-0112451352510.1016/j.aej.2025.03.106A novel reconstruction-based video anomaly detection with idempotent generative networkWenmin Dong0Lifeng Zhang1Wenjuan Shi2Xiangwei Zheng3Yuang Zhang4School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250358, ChinaCorresponding author at: School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.; School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250358, ChinaVideo anomaly detection (VAD) is vital in intelligent security for public safety. Reconstruction-based VAD has received increasing research attention, but faces challenges such as missing anomalies for the reconstruction error as a criterion, and information loss when suppressing anomalous data, existing methods also struggle to detect unseen anomalies. We propose a novel reconstruction-based video anomaly detection with idempotent generative network (RVADIGN), which is composed of the novel reconstruction module namely PSVAE and an idempotent loss term (IGN). Specifically, video frames are reconstructed within PSVAE. During this process, skip connections are established between the encoder and decoder to enhance contextual understanding. Finite Scalar Quantization (FSQ) layer is designed to discretize the encoder’s output, preserving key discriminative features. Meanwhile, the Pyramid Deformation Module (PDM), as an integral part of PSVAE, computes offset maps of original video frames for anomaly detection supplementation. Alongside PSVAE, idempotence is introduced as a regularity term, which projects the anomaly information back to the estimated manifolds of the target distribution, improves the adaptability and stability of the reconstruction method in different videos. Extensive experimental results demonstrate that our method outperforms other state-of-the-art VAD methods, achieving 99.03%, 92.40%, and 77.20% AUC on UCSD Ped2, CUHK Avenue, and ShanghaiTech, respectively.http://www.sciencedirect.com/science/article/pii/S1110016825004144Video anomaly detectionUnsupervised learningIdempotent generative networksQuantitative layerReconstruction supplementation |
| spellingShingle | Wenmin Dong Lifeng Zhang Wenjuan Shi Xiangwei Zheng Yuang Zhang A novel reconstruction-based video anomaly detection with idempotent generative network Alexandria Engineering Journal Video anomaly detection Unsupervised learning Idempotent generative networks Quantitative layer Reconstruction supplementation |
| title | A novel reconstruction-based video anomaly detection with idempotent generative network |
| title_full | A novel reconstruction-based video anomaly detection with idempotent generative network |
| title_fullStr | A novel reconstruction-based video anomaly detection with idempotent generative network |
| title_full_unstemmed | A novel reconstruction-based video anomaly detection with idempotent generative network |
| title_short | A novel reconstruction-based video anomaly detection with idempotent generative network |
| title_sort | novel reconstruction based video anomaly detection with idempotent generative network |
| topic | Video anomaly detection Unsupervised learning Idempotent generative networks Quantitative layer Reconstruction supplementation |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825004144 |
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