Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection
This article introduces a novel anomaly detector for intelligent monitoring systems, leveraging multiple assessment baselines, including conventional, frame-based, and scenario-based approaches, to enhance anomaly detection. The integration of these baselines improves detection accuracy and contextu...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
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| Online Access: | https://ieeexplore.ieee.org/document/10807086/ |
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| _version_ | 1832583173797576704 |
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| author | Abbas Mahbod Henry Leung |
| author_facet | Abbas Mahbod Henry Leung |
| author_sort | Abbas Mahbod |
| collection | DOAJ |
| description | This article introduces a novel anomaly detector for intelligent monitoring systems, leveraging multiple assessment baselines, including conventional, frame-based, and scenario-based approaches, to enhance anomaly detection. The integration of these baselines improves detection accuracy and contextual understanding of anomalies. A key feature of the proposed methodology is the incorporation of the Semi-Siam technique, a semi-supervised few-shot learning approach, which significantly boosts performance in scenarios with limited training data. Extensive simulations on multiple datasets demonstrate the proposed system’s effectiveness and substantial improvements over existing techniques. The results indicate that this methodology offers a robust and efficient solution for real-world video anomaly detection applications, such as the City of Calgary dataset, providing significant advancements in detection accuracy and adaptability. |
| format | Article |
| id | doaj-art-87ae0ef0bdec4e548946430ea6021b64 |
| institution | Kabale University |
| issn | 2768-7236 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Instrumentation and Measurement |
| spelling | doaj-art-87ae0ef0bdec4e548946430ea6021b642025-01-29T00:01:35ZengIEEEIEEE Open Journal of Instrumentation and Measurement2768-72362025-01-01411310.1109/OJIM.2024.351761410807086Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly DetectionAbbas Mahbod0https://orcid.org/0000-0002-8163-208XHenry Leung1https://orcid.org/0000-0002-5984-107XDepartment of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, CanadaDepartment of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, CanadaThis article introduces a novel anomaly detector for intelligent monitoring systems, leveraging multiple assessment baselines, including conventional, frame-based, and scenario-based approaches, to enhance anomaly detection. The integration of these baselines improves detection accuracy and contextual understanding of anomalies. A key feature of the proposed methodology is the incorporation of the Semi-Siam technique, a semi-supervised few-shot learning approach, which significantly boosts performance in scenarios with limited training data. Extensive simulations on multiple datasets demonstrate the proposed system’s effectiveness and substantial improvements over existing techniques. The results indicate that this methodology offers a robust and efficient solution for real-world video anomaly detection applications, such as the City of Calgary dataset, providing significant advancements in detection accuracy and adaptability.https://ieeexplore.ieee.org/document/10807086/Anomaly detectionintelligent systemmultiple baselines analysistraffic monitoringurban transportation |
| spellingShingle | Abbas Mahbod Henry Leung Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection IEEE Open Journal of Instrumentation and Measurement Anomaly detection intelligent system multiple baselines analysis traffic monitoring urban transportation |
| title | Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection |
| title_full | Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection |
| title_fullStr | Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection |
| title_full_unstemmed | Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection |
| title_short | Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection |
| title_sort | semi siam a novel intelligent monitoring system with a multibaseline video anomaly detection |
| topic | Anomaly detection intelligent system multiple baselines analysis traffic monitoring urban transportation |
| url | https://ieeexplore.ieee.org/document/10807086/ |
| work_keys_str_mv | AT abbasmahbod semisiamanovelintelligentmonitoringsystemwithamultibaselinevideoanomalydetection AT henryleung semisiamanovelintelligentmonitoringsystemwithamultibaselinevideoanomalydetection |