用RFID信号识别包裹材质，防偷窃更精准
Material Identification Via RFID For Smart Shopping
- 利用信号强度和相位变化，训练神经网络识别七种容器
- 单次读取准确率达74%，1秒样本达89%准确率
- 可实时标记可疑行为，适合已部署RFID的无人店
无收银商店依赖计算机视觉和RFID标签将顾客与商品关联，但藏在背包、口袋或包中的商品会带来盗窃风险。本文提出一种系统，通过不同容器对射频信号的衰减与散射特性，将现有RFID标签转变为材料传感器。利用RSSI和相位角数据，训练神经网络以分类七类常见容器。在模拟零售环境中，该模型在1秒采样下达到89%准确率，单次读取达74%准确率。结合距离测量，系统在0.3-2米标签与读取器间距下实现82%准确率。部署于过道或门口等关键位置时，可实时标记可疑事件，触发摄像头筛查或工作人员干预。结合材料识别与视觉追踪，该系统可利用现有基础设施实现主动防盗。
原文摘要 · Abstract (English)
Cashierless stores rely on computer vision and RFID tags to associate shoppers with items, but concealed items placed in backpacks, pockets, or bags create challenges for theft prevention. We introduce a system that turns existing RFID tagged items into material sensors by exploiting how different containers attenuate and scatter RF signals. Using RSSI and phase angle, we trained a neural network to classify seven common containers. In a simulated retail environment, the model achieves 89% accuracy with one second samples and 74% accuracy from single reads. Incorporating distance measurements, our system achieves 82% accuracy across 0.3-2m tag to reader separations. When deployed at aisle or doorway choke points, the system can flag suspicious events in real time, prompting camera screening or staff intervention. By combining material identification with computer vision tracking, our system provides proactive loss prevention for cashierless retail while utilizing existing infrastructure.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。