  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.
