Lightweight Pose-Based Shoplifting Detection for Retail: A Confidence-Weighted LSTM Approach using Human Pose Trajectories

Lightweight Pose-Based Shoplifting Detection for Retail: A Confidence-Weighted LSTM Approach using Human Pose Trajectories | 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 Lightweight Pose-Based Shoplifting Detection for Retail: A Confidence-Weighted LSTM Approach using Human Pose Trajectories Hesham M. Shehata, Mohamed El Bassat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8245489/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 We present a privacy-preserving shoplifting detector that operates solely on human pose trajectories, avoiding any identifiable imagery. The system standardizes 2D keypoints, derives kinematics-aware features (torso-normalized positions, velocities, and limb-orientation cues), and applies attention over a lightweight, causal LSTM to capture short, subtle actions typical of retail incidents. To handle pose noise and label ambiguity, we introduce a confidence-weighted training objective that scales each window’s contribution by per-sample pose quality (joint confidence, visible-joint ratio, limb-stability), preceded by a sliding-window quality-control stage with bounded interpolation and smoothing.We evaluate on a curated UCF-Crime shoplifting subset—30 videos after strict pose-quality control—and, using our Focal-weighted model, achieve 71% weighted accuracy, 77.23% AUC-ROC at the person-ID (PID) level, and 85% recall. Leveraging our PID testing aggregation method—averaging probabilities across QC-passed windows per individual—the approach exhibits notable robustness and stable performance across varied scenarios, delivering superior/comparable results to state-of-the-art pose-based anomaly detectors while remaining truly lightweight for seamless edge deployment. The classifier runs in real time on commodity CPUs when provided precomputed keypoints, enabling use where privacy policies prohibit pixel processing. Importantly, PID-level aggregation combined with confidence weighting materially reduces false alarms in deployment, improving practical reliability without increasing computational cost and offering a compact, privacy-compliant solution for multi-camera retail environments. Shoplifting Detection Human Pose Estimation LSTM Privacy-Preserving AI Computer Vision Full Text Additional Declarations No competing interests reported. Supplementary Files TP1.mp4 TP2.mp4 TP3.mp4 FP2.mp4 FP3.mp4 suspicious61757938200shelf1.mp4 suspicious71757938254shelf1.mp4 suspicious151757938322shelf1.mp4 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-8245489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561215989,"identity":"8080e4f1-f534-482e-ba26-e8fbb79490a8","order_by":0,"name":"Hesham M. 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The system standardizes 2D keypoints, derives kinematics-aware features (torso-normalized positions, velocities, and limb-orientation cues), and applies attention over a lightweight, causal LSTM to capture short, subtle actions typical of retail incidents. To handle pose noise and label ambiguity, we introduce a confidence-weighted training objective that scales each window\u0026rsquo;s contribution by per-sample pose quality (joint confidence, visible-joint ratio, limb-stability), preceded by a sliding-window quality-control stage with bounded interpolation and smoothing.We evaluate on a curated UCF-Crime shoplifting subset\u0026mdash;30 videos after strict pose-quality control\u0026mdash;and, using our Focal-weighted model, achieve 71% weighted accuracy, 77.23% AUC-ROC at the person-ID (PID) level, and 85% recall. Leveraging our PID testing aggregation method\u0026mdash;averaging probabilities across QC-passed windows per individual\u0026mdash;the approach exhibits notable robustness and stable performance across varied scenarios, delivering superior/comparable results to state-of-the-art pose-based anomaly detectors while remaining truly lightweight for seamless edge deployment. The classifier runs in real time on commodity CPUs when provided precomputed keypoints, enabling use where privacy policies prohibit pixel processing. 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