Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation
Detecting anomaly patterns in videos is a challenging task due to complex scenes, huge diversity of anomalies, and fuzzy nature of the task. With advent of technology, tremendous size of visual data is being generated by video surveillance systems, which makes harder to search, analyze, and detect a...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11071705/ |
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| author | Burcak Asal Ahmet Burak Can |
| author_facet | Burcak Asal Ahmet Burak Can |
| author_sort | Burcak Asal |
| collection | DOAJ |
| description | Detecting anomaly patterns in videos is a challenging task due to complex scenes, huge diversity of anomalies, and fuzzy nature of the task. With advent of technology, tremendous size of visual data is being generated by video surveillance systems, which makes harder to search, analyze, and detect anomalies on video data by human operators. In this paper, we introduce three relational distillation approaches to handle both robust detection of anomalous events and gradual adaptation to different anomaly patterns in new videos while not forgetting anomaly patterns learned from the previous video data. In order to realize these concepts, we propose a unique attention mechanism with feature and relation based knowledge distillation methods. We adapted our knowledge distillation methods to two state-of-the-art models designed for anomaly detection task. Our extensive experiments on two public datasets show that not only our best version model achieves robust performance with a frame-level AUC of 80.22 on UCF-Crime and video-level AUC of 78.20 on RWF-2000 datasets but also the proposed distillation methods improve the performance while reducing catastrophic forgetting problem. |
| format | Article |
| id | doaj-art-a3766471a76943b6b2dfc3d7d284b7b5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a3766471a76943b6b2dfc3d7d284b7b52025-08-20T03:29:02ZengIEEEIEEE Access2169-35362025-01-011311717011718510.1109/ACCESS.2025.358598411071705Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge DistillationBurcak Asal0https://orcid.org/0009-0003-3729-8170Ahmet Burak Can1https://orcid.org/0000-0002-0101-6878Department of Computer Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, TürkiyeDepartment of Computer Engineering, Hacettepe University, Ankara, TürkiyeDetecting anomaly patterns in videos is a challenging task due to complex scenes, huge diversity of anomalies, and fuzzy nature of the task. With advent of technology, tremendous size of visual data is being generated by video surveillance systems, which makes harder to search, analyze, and detect anomalies on video data by human operators. In this paper, we introduce three relational distillation approaches to handle both robust detection of anomalous events and gradual adaptation to different anomaly patterns in new videos while not forgetting anomaly patterns learned from the previous video data. In order to realize these concepts, we propose a unique attention mechanism with feature and relation based knowledge distillation methods. We adapted our knowledge distillation methods to two state-of-the-art models designed for anomaly detection task. Our extensive experiments on two public datasets show that not only our best version model achieves robust performance with a frame-level AUC of 80.22 on UCF-Crime and video-level AUC of 78.20 on RWF-2000 datasets but also the proposed distillation methods improve the performance while reducing catastrophic forgetting problem.https://ieeexplore.ieee.org/document/11071705/AR-Netcomputer visionGCNknowledge distillationrelational approachesvideo anomaly detection |
| spellingShingle | Burcak Asal Ahmet Burak Can Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation IEEE Access AR-Net computer vision GCN knowledge distillation relational approaches video anomaly detection |
| title | Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation |
| title_full | Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation |
| title_fullStr | Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation |
| title_full_unstemmed | Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation |
| title_short | Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation |
| title_sort | adaptive video anomaly detection by attention based relational knowledge distillation |
| topic | AR-Net computer vision GCN knowledge distillation relational approaches video anomaly detection |
| url | https://ieeexplore.ieee.org/document/11071705/ |
| work_keys_str_mv | AT burcakasal adaptivevideoanomalydetectionbyattentionbasedrelationalknowledgedistillation AT ahmetburakcan adaptivevideoanomalydetectionbyattentionbasedrelationalknowledgedistillation |