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Cashier-free shopping systems like Amazon Go improve shopping experience, butcan require significant store redesign. In this paper, we propose Grab, apractical system that leverages existing infrastructure and devices to enablecashier-free shopping. Grab needs to accurately identify and track customers,and associate each shopper with items he or she retrieves from shelves. To dothis, it uses a keypoint-based pose tracker as a building block foridentification and tracking, develops robust feature-based face trackers, andalgorithms for associating and tracking arm movements. It also uses aprobabilistic framework to fuse readings from camera, weight and RFID sensorsin order to accurately assess which shopper picks up which item. In experimentsfrom a pilot deployment in a retail store, Grab can achieve over 90% precisionand recall even when 40% of shopping actions are designed to confuse thesystem. Moreover, Grab has optimizations that help reduce investment incomputing infrastructure four-fold.

Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping