A Region-Based Deep Learning Approach to Automated Retail Checkout

Maged Shoman, Armstrong Aboah, Alex Morehead, Ye Duan, Abdulateef Daud, Yaw Adu-Gyamfi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 3210-3215

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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Shoman_2022_CVPR, author = {Shoman, Maged and Aboah, Armstrong and Morehead, Alex and Duan, Ye and Daud, Abdulateef and Adu-Gyamfi, Yaw}, title = {A Region-Based Deep Learning Approach to Automated Retail Checkout}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {3210-3215} }