
Periodic Adaptation for Edge Shoplifting Detection
This paper introduces a periodic adaptation framework for pose-based, unsupervised shoplifting detection deployable on IoT edge devices in retail. It releases RetailS, a large-scale real-world dataset from multi-day, multi-camera store surveillance. The approach outperforms offline baselines in 91.6% of cases with training under 30 minutes on edge hardware.






