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arXiv · 2504.19970

Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Pose

Narges Rashvand·Ghazal Alinezhad Noghre·Armin Danesh Pazho·Babak Rahimi Ardabili·Hamed Tabkhi
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Abstract

Shoplifting remains a costly issue for the retail sector, but traditional surveillance systems, which are mostly based on human monitoring, are still largely ineffective, with only about 2% of shoplifters being arrested. Existing AI-based approaches rely on pixel-level video analysis which raises privacy concerns, is sensitive to environmental variations, and demands significant computational resources. To address these limitations, we introduce Shopformer, a novel transformer-based model that detects shoplifting by analyzing pose sequences rather than raw video. We propose a custom tokenization strategy that converts pose sequences into compact embeddings for efficient transformer processing. To the best of our knowledge, this is the first pose-sequence-based transformer model for shoplifting detection. Evaluated on real-world pose data, our method outperforms state-of-the-art anomaly detection models, offering a privacy-preserving, and scalable solution for real-time retail surveillance. The code base for this work is available at https://github.com/TeCSAR-UNCC/Shopformer.

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Narges Rashvand, Ghazal Alinezhad Noghre, Armin Danesh Pazho, Babak Rahimi Ardabili, Hamed Tabkhi. 2025-04-28. Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Pose. https://arxiv.org/abs/2504.19970

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