Retail checkout systems employed at supermarkets primarily rely on barcode scanners, with some utilizing QR codes, to identify the items being purchased. These methods are time-consuming in practice, require a certain level of human supervision, and involve waiting in long queues. In this regard, we propose a system, that we call ARC, which aims at making the process of check-out at retail store counters faster, autonomous, and more convenient, while reducing dependency on a human operator. The approach makes use of a computer vision-based system, with a Convolutional Neural Network at its core, which scans objects placed beneath a webcam for identification. To evaluate the proposed system, we curated an image dataset of one-hundred local retail items of various categories. Within the given assumptions and considerations, the system achieves a reasonable test-time accuracy, pointing towards an ambitious future for the proposed setup. The project code and the dataset are made publicly available.
@article{arxiv.2104.02832,
title = {ARC: A Vision-based Automatic Retail Checkout System},
author = {Syed Talha Bukhari and Abdul Wahab Amin and Muhammad Abdullah Naveed and Muhammad Rzi Abbas},
journal= {arXiv preprint arXiv:2104.02832},
year = {2021}
}
Comments
Work was done during the academic year 2017-2018 as a Senior Year (undergraduate) Project (thesis)