Publication Details
PersonGONE: Image Inpainting for Automated Checkout Solution
Špaňhel Jakub, Ing., Ph.D. (DCGM)
Herout Adam, prof. Ing., Ph.D. (DCGM)
automatic checkout, product counting, image inpainting, object detection, object
tracking
In this paper, we present a solution for automatic checkout in a retail store as
a part of AI City Challenge 2022. We propose a novel approach that uses the
removal of unwanted objects in this case, body parts of operating staff, which
are localized and further removed from video by an image inpainting method.
Afterwards, a neural network detector can detect products with a decreased
detection false positive rate. A part of our solution is also automatic detection
of ROI (the place where products are shown to the system). We reached 0.4167
F1-Score with 0.3704 precision and 0.4762 recall which placed us at the 7th place
of AI City Challenge 2022 in corresponding Track 4. The code is made public and
available on GitHub.
@inproceedings{BUT178943,
author="Vojtěch {Bartl} and Jakub {Špaňhel} and Adam {Herout}",
title="PersonGONE: Image Inpainting for Automated Checkout Solution",
booktitle="2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
year="2022",
series="IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
volume="2022",
number="7",
pages="3114--3122",
publisher="IEEE Computer Society",
address="New Orleans, LA",
doi="10.1109/CVPRW56347.2022.00351",
issn="2160-7516",
url="https://ieeexplore.ieee.org/document/9857198"
}