ARC: A Vision-based Automatic Retail Checkout System
This addresses the issue of time-consuming checkout processes for retail customers and operators, but it is incremental as it builds on existing computer vision methods.
The authors tackled the problem of slow, human-dependent retail checkout by proposing ARC, a vision-based system using a Convolutional Neural Network to scan items, achieving a reasonable test-time accuracy on a curated dataset of 100 local retail items.
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.