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

arXiv:2603.04723v1 Announce Type: new 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-