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

• URL: http://arxiv.org/abs/2001.01033v1
• Date: Sat, 4 Jan 2020 04:12:06 GMT
• Title: Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping
• Authors: Xiaochen Liu, Yurong Jiang, Kyu-Han Kim, Ramesh Govindan
• Abstract summary: We propose Grab, a 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. In experiments from a pilot deployment in a retail store, Grab can achieve over 90% precision and recall.
• Score: 5.777092390527491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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