Contrastive-regularized U-Net for video anomaly detection

Gan, Kian Yu and Cheng, Yu Tong and Tan, Hung-Khoon and Ng, Hui-Fuang and Leung, Maylor Karhang and Chuah, Joon Huang (2023) Contrastive-regularized U-Net for video anomaly detection. IEEE Access, 11. pp. 36658-36671. ISSN 2169-3536, DOI

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Video anomaly detection aims to identify anomalous segments in a video. It is typically trained with weakly supervised video-level labels. This paper focuses on two crucial factors affecting the performance of video anomaly detection models. First, we explore how to capture the local and global temporal dependencies more effectively. Previous architectures are effective at capturing either local and global information, but not both. We propose to employ a U-Net like structure to model both types of dependencies in a unified structure where the encoder learns global dependencies hierarchically on top of local ones; then the decoder propagates this global information back to the segment level for classification. Second, overfitting is a non-trivial issue for video anomaly detection due to limited training data. We propose weakly supervised contrastive regularization which adopts a feature-based approach to regularize the network. Contrastive regularization learns more generalizable features by enforcing inter-class separability and intra-class compactness. Extensive experiments on the UCF-Crime dataset shows that our approach outperforms several state-of-the-art methods.

Item Type: Article
Funders: Fundamental Research Grant Scheme (FRGS) through the Ministry of Higher Education (MOHE) of Malaysia (FRGS/1/2018/ICT02/UTAR/02/03)
Uncontrolled Keywords: Feature extraction; Deep learning; Anomaly detection; Image reconstruction; Image segmentation; Supervised learning; Training data; video anomaly detection; weakly supervised learning; contrastive-based regularization; multi-instance learning; deep learning
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 22 Nov 2023 06:02
Last Modified: 22 Nov 2023 06:02

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