[2603.04723] From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security

Computer Science > Artificial Intelligence arXiv:2603.04723 (cs) [Submitted on 5 Mar 2026] 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 View a PDF of the paper titled From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security, by Shanle Yao and 3 other authors View PDF HTML (experimental) 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 ...