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

arXiv: https://arxiv.org/abs/2001.01033 · HTML: https://mlsanity.com/paper/2001.01033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Optimizing Camera Placements for Overlapped Coverage with 3D Camera Projections](https://mlsanity.com/paper/2203.10479/) (2022)

## Abstract (arXiv)

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.