From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security
#shoplifting detection #pose-based analysis #periodic adaptation #real-world security #retail technology
📌 Key Takeaways
- The article introduces a new method for shoplifting detection using pose-based analysis.
- It shifts from offline to periodic adaptation to improve real-world retail security.
- The approach aims to enhance detection accuracy by continuously updating models.
- It addresses challenges in dynamic retail environments through adaptive learning.
📖 Full Retelling
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-
🏷️ Themes
Retail Security, AI Adaptation
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2603.04723 [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 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 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04723 [cs.AI] (or arXiv:2603.04723v1 [cs.AI] for thi...
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