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

TL;DR

This paper presents a novel pose-based, unsupervised anomaly detection framework for shoplifting that adapts periodically on edge devices, supported by a new large-scale real-world dataset, enabling scalable and low-latency retail security solutions.

Contribution

It introduces a periodic adaptation framework for pose-based shoplifting detection suitable for IoT deployment and provides a new large-scale dataset, RetailS, for real-world evaluation.

Findings

Outperforms offline baselines in 91.6% of evaluations.

Training updates complete in under 30 minutes on edge hardware.

Supports scalable, low-latency shoplifting detection in retail environments.

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.