论文标题
抓取：快速准确的传感器处理，用于无收银员购物
Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping
论文作者
论文摘要
亚马逊（Amazon）这样的无收银购物系统改善购物体验，但可能需要重大的商店重新设计。在本文中，我们提出了Grab，这是一个实用的系统，该系统利用现有的基础设施和设备来实现无收银员购物。 Grab需要准确地识别和跟踪客户，并将每个购物者与他或她从货架上取回的物品相关联。为此，它使用基于按键的姿势跟踪器作为识别和跟踪的构建块，开发基于功能的良好面部跟踪器以及用于关联和跟踪ARM运动的算法。它还使用概率框架来融合相机，重量和RFID传感器的读数，以便准确评估哪个购物者可以拾取哪个项目。在零售商店的试点部署的实验中，Grab可以达到超过90％的精度，即使40％的购物动作旨在使系统混淆。此外，Grab具有优化，有助于减少四倍的计算基础架构的投资。
Cashier-free shopping systems like Amazon Go improve shopping experience, but can require significant store redesign. In this paper, we propose Grab, a practical system that leverages existing infrastructure and devices to enable cashier-free shopping. Grab needs to accurately identify and track customers, and associate each shopper with items he or she retrieves from shelves. To do this, it uses a keypoint-based pose tracker as a building block for identification and tracking, develops robust feature-based face trackers, and algorithms for associating and tracking arm movements. It also uses a probabilistic framework to fuse readings from camera, weight and RFID sensors in order to accurately assess which shopper picks up which item. In experiments from a pilot deployment in a retail store, Grab can achieve over 90% precision and recall even when 40% of shopping actions are designed to confuse the system. Moreover, Grab has optimizations that help reduce investment in computing infrastructure four-fold.
