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

arXiv: https://arxiv.org/abs/2504.19970 · HTML: https://mlsanity.com/paper/2504.19970/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [Normalizing Flows for Human Pose Anomaly Detection](https://mlsanity.com/paper/2211.10946/) (2022)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction](https://mlsanity.com/paper/2406.15395/) (2024)
- [VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer](https://mlsanity.com/paper/2311.06623/) (2023)
- [Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems](https://mlsanity.com/paper/2210.08057/) (2022)
- [Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety](https://mlsanity.com/paper/2302.04310/) (2023)
- [Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety](https://mlsanity.com/paper/2212.12936/) (2022)
- [Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency](https://mlsanity.com/paper/2408.02164/) (2024)
- [Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformer](https://mlsanity.com/paper/2408.15185/) (2024)
- [Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark](https://mlsanity.com/paper/2501.06591/) (2025)
- [Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques](https://mlsanity.com/paper/2502.13256/) (2025)
- [Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders](https://mlsanity.com/paper/2504.14122/) (2025)

## Built on by (most cited first, 6 of 6)

- [From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security](https://mlsanity.com/paper/2603.04723/) (2026)
- [Adversarially-Refined VQ-GAN with Dense Motion Tokenization for Spatio-Temporal Heatmaps](https://mlsanity.com/paper/2509.19252/) (2025)
- [Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild](https://mlsanity.com/paper/2603.04727/) (2026)
- [From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection](https://mlsanity.com/paper/2604.09327/) (2026)
- [MA-CBP: A Criminal Behavior Prediction Framework Based on Multi-Agent Asynchronous Collaboration](https://mlsanity.com/paper/2508.06189/) (2025)
- [VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection](https://mlsanity.com/paper/2608.05069/) (2026)

## Abstract (arXiv)

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.