From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security

• URL: http://arxiv.org/abs/2603.04723v1
• Date: Thu, 05 Mar 2026 01:53:06 GMT
• Title: From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security
• Authors: Shanle Yao, Narges Rashvand, Armin Danesh Pazho, Hamed Tabkhi,
• Abstract summary: Shoplifting is a growing operational and economic challenge for retailers.<n>In this paper, we cast shoplifting detection as a pose-based, unsupervised video anomaly detection problem.<n>We introduce a periodic adaptation framework designed for on-site Internet of Things (IoT) deployment.
• Score: 9.42132060759461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shoplifting is a growing operational and economic challenge for retailers, with incidents rising and losses increasing despite extensive video surveillance. Continuous human monitoring is infeasible, motivating automated, privacy-preserving, and resource-aware detection solutions. In this paper, we cast shoplifting detection as a pose-based, unsupervised video anomaly detection problem and introduce a periodic adaptation framework designed for on-site Internet of Things (IoT) deployment. Our approach enables edge devices in smart retail environments to adapt from streaming, unlabeled data, supporting scalable and low-latency anomaly detection across distributed camera networks. To support reproducibility, we introduce RetailS, a new large-scale real-world shoplifting dataset collected from a retail store under multi-day, multi-camera conditions, capturing unbiased shoplifting behavior in realistic IoT settings. For deployable operation, thresholds are selected using both F1 and H_PRS scores, the harmonic mean of precision, recall, and specificity, during data filtering and training. In periodic adaptation experiments, our framework consistently outperformed offline baselines on AUC-ROC and AUC-PR in 91.6% of evaluations, with each training update completing in under 30 minutes on edge-grade hardware, demonstrating the feasibility and reliability of our solution for IoT-enabled smart retail deployment.

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