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

TL;DR

This paper cast shoplifting detection as a pose-based, unsupervised video anomaly detection problem and introduces a periodic adaptation framework designed for on-site Internet of Things (IoT) deployment, enabling edge devices in smart retail environments to adapt from streaming, unlabeled data.

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.

Figures (9)

• Figure 1: A conceptual overview of an IoT-enabled shoplifting detection system with continual unsupervised anomaly detection. Frames (F) from distributed surveillance cameras are preprocessed at the edge to produce annotations (A), including bounding boxes (BB), track IDs (ID), and human poses. The anomaly detector filters frames for real-time screening and outputs anomaly results (AR). A collector accumulates pseudo-filtered frames F[n] for continual unsupervised adaptation. After each training cycle, updated model weights (W) are pushed back to the edge for the next cycle of detection.
• Figure 2: A conceptual overview of our IoT-oriented continual unsupervised anomaly detection pipeline with pseudo filtering, collection, and training. Model updates are designed to run incrementally on edge-grade devices, enabling scalable deployment across distributed surveillance nodes.
• Figure 3: Bird’s-eye view of the retail store in our RetailS dataset, showing six IoT-connected camera locations and their coverage areas.
• Figure 4: Two six-frame shoplifting sequences are illustrated, with the top row showing concealment of an item in a pocket and the bottom row showing placement of an item in a bag while standing. Anonymized pose sequences ensure privacy while enabling IoT deployment.
• Figure 5: Distribution of shoplifting instances across six IoT-connected cameras. Real-world events cluster in specific zones, while staged scenarios ensure coverage across all views.