Spatio-temporal anomaly detection for real-time video surveillance using SpatioGuard-YOLO framework.
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| Title: | Spatio-temporal anomaly detection for real-time video surveillance using SpatioGuard-YOLO framework. |
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| Authors: | Krishnan, Sreedevi R.1 (AUTHOR) sreedevi.cs@adishankara.ac.in, Amudha, P2 (AUTHOR) amudha_cse@avinuty.ac.in |
| Source: | Multimedia Tools & Applications. Jun2026, Vol. 85 Issue 6, p1-33. 33p. |
| Abstract: | Detecting anomalous events in real-time video surveillance is challenging due to cluttered backgrounds, overlapping objects, low-light conditions, and complex spatio-temporal variations. To address these issues, this paper proposes SpatioGuard-YOLO, an integrated framework that combines adaptive contrast enhancement, efficient object detection, and multi-scale spatial feature learning for robust video anomaly detection. A Dynamic Contrast Enhancer is introduced to improve visibility under adverse lighting conditions, while a novel Spatially Expanded Neural Network captures both global contextual information and fine-grained local details without compromising real-time performance. The proposed framework is evaluated on a real-time CCTV surveillance dataset designed for anomaly detection in pedestrian-restricted pathways, demonstrating strong generalization under diverse environmental conditions. By integrating these components with a YOLO-based detection pipeline, the proposed framework effectively reduces false detections in complex surveillance environments. Experimental results demonstrate that SpatioGuard-YOLO achieves high detection accuracy, exceeding 99% overall performance and outperforming existing state-of-the-art methods, confirming its effectiveness and reliability for real-world video surveillance applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194197604 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Spatio-temporal anomaly detection for real-time video surveillance using SpatioGuard-YOLO framework. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Krishnan%2C+Sreedevi+R%2E%22">Krishnan, Sreedevi R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sreedevi.cs@adishankara.ac.in</i><br /><searchLink fieldCode="AR" term="%22Amudha%2C+P%22">Amudha, P</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> amudha_cse@avinuty.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Jun2026, Vol. 85 Issue 6, p1-33. 33p. – Name: Abstract Label: Abstract Group: Ab Data: Detecting anomalous events in real-time video surveillance is challenging due to cluttered backgrounds, overlapping objects, low-light conditions, and complex spatio-temporal variations. To address these issues, this paper proposes SpatioGuard-YOLO, an integrated framework that combines adaptive contrast enhancement, efficient object detection, and multi-scale spatial feature learning for robust video anomaly detection. A Dynamic Contrast Enhancer is introduced to improve visibility under adverse lighting conditions, while a novel Spatially Expanded Neural Network captures both global contextual information and fine-grained local details without compromising real-time performance. The proposed framework is evaluated on a real-time CCTV surveillance dataset designed for anomaly detection in pedestrian-restricted pathways, demonstrating strong generalization under diverse environmental conditions. By integrating these components with a YOLO-based detection pipeline, the proposed framework effectively reduces false detections in complex surveillance environments. Experimental results demonstrate that SpatioGuard-YOLO achieves high detection accuracy, exceeding 99% overall performance and outperforming existing state-of-the-art methods, confirming its effectiveness and reliability for real-world video surveillance applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-026-21696-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 1 Titles: – TitleFull: Spatio-temporal anomaly detection for real-time video surveillance using SpatioGuard-YOLO framework. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Krishnan, Sreedevi R. – PersonEntity: Name: NameFull: Amudha, P IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 85 – Type: issue Value: 6 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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