Detail výsledku
PersonGONE: Image Inpainting for Automated Checkout Solution
Špaňhel Jakub, Ing., Ph.D., UPGM (FIT)
Herout Adam, prof. Ing., Ph.D., UPGM (FIT)
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.
automatic checkout, product counting, image inpainting, object detection, object tracking
@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",
journal="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="2160-7508",
url="https://ieeexplore.ieee.org/document/9857198"
}Moderní metody zpracování, analýzy a zobrazování multimediálních a 3D dat, VUT, Vnitřní projekty VUT, FIT-S-20-6460, zahájení: 2020-03-01, ukončení: 2023-02-28, ukončen