Real-Time Surveillance System Using Key Point Detection and Graph Convolutional Networks for Suspicious Activity Recognition

Real-Time Surveillance System Using Key Point Detection and Graph Convolutional Networks for Suspicious Activity Recognition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-Time Surveillance System Using Key Point Detection and Graph Convolutional Networks for Suspicious Activity Recognition M. Archana, S. Kavitha, A. Vani Vathsala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6124369/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Real-time video surveillance is a critical tool for public safety, enabling crime prevention, crowd management, and timely emergency response. However, traditional surveillance systems face challenges in dynamic environments, including occlusions, low-light conditions, and high labor intensity, which limit their accuracy and reliability. This study proposes an advanced real-time surveillance system integrating OpenPose for key point detection and Graph Convolutional Networks (GCNs) for spatio-temporal activity recognition. The system is based on a modular architecture consisting of preprocessing of video, extraction of key points, and real time activity classification, which is capable of effectively identifying suspicious behaviors. On the COCO Keypoints, MPII Human Pose, and a custom surveillance dataset, the system achieved precision of 91.0%, recall of 88.5% and an F1 score of 89.7 and the latency of 0.48 seconds, therefore making it a suitable candidate for real time applications. Performance analyis under occlusions, low light conditions and dynamic environments showed robust drops of ~12%, ~7% and ~5% respectively demonstrating that robustness needs to be improved. We compared our results with baseline methods (OpenPose + LSTM, CNN + LSTM) and achieved significant improvements in all principal key metrics such as mean average precision (mAP) of 74.5%. These results validate the proposed system as a state-of-the-art real time surveillance system capable of dealing with different operational constraints and at the same time provide timely and accurate detection of suspicious activities. As a result of the system’s modular design, scalability and adaptability, it is suitable for deploying in more complex public safety scenarios. Real-time surveillance key point detection deep learning Graph Convolutional Networks suspect identification public safety Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6124369","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":422255349,"identity":"da35d4ae-b796-4cd5-93bf-54df0d74aa1a","order_by":0,"name":"M. 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