Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This project creates an intelligent surveillance system that processes video feeds to detect and analyze mask usage in real-time. Through the combination of computer vision and deep learning models, the system identifies individuals, determines mask compliance, and performs contextual analysis within monitored environments. The system employs a multi-layered architecture: video processing modules analyze frames, YOLO-based detection models identify persons and mask status, and a web interface visualizes results. Among the key innovations are real-time compliance monitoring, demographic estimation capabilities, and comprehensive analytics dashboards. Implementation outcomes demonstrate improved monitoring efficiency, reduction of manual observation needs, and enhanced safety protocol enforcement compared to traditional surveillance methods.
Keywords:
AI Agents, Computer Vision, YOLO, Real-Time Surveillance, OpenCV, Face Detection, Safety Monitoring, Mask Detection
Cite Article:
"Sentinel Vision: Smart AI Surveillance System", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a400-a404, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504057.pdf
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000348
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator