Methods › Computer Vision › Cashier-Free Shopping › Grab

Grab

6 papers tagged archive 2025-07-28

Introduced by Xiaochen Liu et al. in Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Grab is a sensor processing system for 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.

PaperSource

Papers archive 2025-07-28

6 shown of 6, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Clustering1
Decision Making1
Entity Resolution1
Fake News Detection1

Usage over time archive 2025-07-28

Papers per year tagged with Grab: 2020 to 2025, peak 2 2 0 2020: 2 papers 2020 2021: 2 papers 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (6 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Cashier-Free Shopping

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