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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-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,

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Shanle Yao, Narges Rashvand, Armin Danesh Pazho, Hamed Tabkhi · · 1 min read · 2 views
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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-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.

Executive Summary

This article proposes a periodic adaptation framework for detecting shoplifting in retail environments using IoT-enabled video surveillance. The framework, designed for edge devices, adapts from unlabeled streaming data and can detect anomalies in real-time. The authors introduce a new large-scale dataset, RetailS, and demonstrate the efficacy of their approach through comparative analysis with offline baselines. The framework's ability to adapt periodically and operate on edge-grade hardware makes it a promising solution for smart retail deployment. However, the article's scope is limited to shoplifting detection, and its applicability to other retail security challenges is not explored. Despite this, the framework's reliability and scalability make it a valuable contribution to the field of retail security and IoT-enabled anomaly detection.

Key Points

  • ▸ The framework adapts from unlabeled streaming data and operates on edge-grade hardware.
  • ▸ The RetailS dataset provides a new resource for evaluating retail security solutions.
  • ▸ The framework demonstrates superior performance compared to offline baselines in 91.6% of evaluations.

Merits

Strength in adaptability

The framework's ability to adapt from unlabeled streaming data and operate on edge-grade hardware makes it a promising solution for smart retail deployment.

Strength in dataset contribution

The introduction of RetailS dataset provides a valuable resource for evaluating retail security solutions and enables reproducibility of results.

Demerits

Limitation in scope

The framework's applicability is limited to shoplifting detection, and its effectiveness for other retail security challenges is not explored.

Limitation in robustness

The framework's performance relies heavily on the RetailS dataset and may not generalize well to other retail environments or scenarios.

Expert Commentary

This article represents a significant contribution to the field of retail security and IoT-enabled anomaly detection. The framework's adaptability and scalability make it a promising solution for smart retail deployment. However, the article's scope is limited, and its applicability to other retail security challenges is not explored. To further strengthen the framework, the authors could consider incorporating additional retail security challenges and exploring its generalizability to other retail environments. Furthermore, the framework's performance relies heavily on the RetailS dataset, and its robustness in real-world scenarios should be investigated.

Recommendations

  • ✓ Future research should explore the framework's applicability to other retail security challenges and its generalizability to different retail environments.
  • ✓ The framework's robustness in real-world scenarios should be investigated through additional experiments and comparisons with other anomaly detection methods.

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