FusedVision: A Knowledge-Infusing Approach for Practical Anomaly Detection in Real-World Surveillance Videos
Dawoud, Khaled Zaheer, Muhammad Zaigham Khan, Mustaqeem Nandakumar, Karthik Saddik, Abdulmotaleb El Khan, Muhammad Haris
Dawoud, Khaled
Zaheer, Muhammad Zaigham
Khan, Mustaqeem
Nandakumar, Karthik
Saddik, Abdulmotaleb El
Khan, Muhammad Haris
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Journal Issue
Abstract
Object-centric approaches have gained attention as effective one-class classification methods for detecting anomalies in videos. These approaches rely on using an object detector to isolate all objects in the frames and subsequently leveraging either the objects themselves or their interactions to train a learning system. In this study, we put forth a novel perspective towards anomaly detection by proposing a branched network architecture that employs both an object detector and a normalcy learning model, working together in tandem to more effectively identify anomalies within the data. Through extensive experimentation, we analyze the optimal fusion mechanism as well as anomaly scoring proposed in our branched approach. Our approach is more practical towards realworld applications of anomaly detection where infusion of the knowledge about anticipated anomalies may result in better performance while maintaining a baseline performance nonetheless. To evaluate the general applicability of our approach, we integrate it with multiple existing recent anomaly detection methods and assess its efficacy on three widely used anomaly detection datasets: ShanghaiTech, Avenue, and Ped2. Our proposed approach noticeably outperforms existing methods, demonstrating its effectiveness in detecting anomalies across a range of contexts. The implementation of our method is available at https://github.com/kdawoud91/FusedVision.
Co-author(s)
Citation
K. Dawoud, M. Z. Zaheer, M. Khan, K. Nandakumar, A. E. Saddik and M. H. Khan, "FusedVision: A Knowledge-Infusing Approach for Practical Anomaly Detection in Real-World Surveillance Videos," 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA, 2025, pp. 4036-4046, doi: 10.1109/CVPRW67362.2025.00388.
Source
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Conference
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2025
Keywords
Anomaly detection in surveillance videos, Anomaly in videos
Subjects
Source
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2025
Publisher
IEEE