用人体姿态序列检测盗窃，更隐私、更高效。
Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Pose
- 基于姿态序列而非视频，用Transformer建模行为模式。
- 在真实数据上优于现有异常检测模型，准确率显著提升。
- 适合需要隐私保护的零售安防场景，计算开销小。
商店盗窃对零售业造成巨大损失，但传统监控系统依赖人工，仅有约2%的盗窃行为被抓获。现有基于AI的方法多依赖像素级视频分析，存在隐私风险、对环境变化敏感且计算成本高。为此，我们提出Shopformer，一种基于Transformer的姿态序列分析模型，用于检测盗窃行为。通过自定义分词策略，将姿态序列转化为紧凑嵌入，实现高效处理。据我们所知，这是首个基于姿态序列的Transformer盗窃检测模型。在真实姿态数据上的评估表明，该方法优于当前最先进的异常检测模型，提供了一种隐私友好且可扩展的实时零售监控方案。代码已公开于https://github.com/TeCSAR-UNCC/Shopformer。
原文摘要 · Abstract (English)
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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