A Region-Based Deep Learning Approach to Automated Retail Checkout
Abstract
Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking. Towards this end, reliable deep learning models that enable automated product counting for fast customer checkout can make this goal a reality. In this work, we propose a novel, region-based deep learning approach to automate product counting using a customized YOLOv5 object detection pipeline and the DeepSORT algorithm. Our results on challenging, real-world test videos demonstrate that our method can generalize its predictions to a sufficient level of accuracy and with a fast enough runtime to warrant deployment to real-world commercial settings. Our proposed method won 4th place in the 2022 AI City Challenge, Track 4, with an F1 score of 0.4400 on experimental validation data.
Cite
Text
Shoman et al. "A Region-Based Deep Learning Approach to Automated Retail Checkout." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00362Markdown
[Shoman et al. "A Region-Based Deep Learning Approach to Automated Retail Checkout." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/shoman2022cvprw-regionbased/) doi:10.1109/CVPRW56347.2022.00362BibTeX
@inproceedings{shoman2022cvprw-regionbased,
title = {{A Region-Based Deep Learning Approach to Automated Retail Checkout}},
author = {Shoman, Maged and Aboah, Armstrong and Morehead, Alex and Duan, Ye and Daud, Abdulateef and Adu-Gyamfi, Yaw},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2022},
pages = {3209-3214},
doi = {10.1109/CVPRW56347.2022.00362},
url = {https://mlanthology.org/cvprw/2022/shoman2022cvprw-regionbased/}
}