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BARTL, V. ŠPAŇHEL, J. HEROUT, A.
Original Title
PersonGONE: Image Inpainting for Automated Checkout Solution
Type
conference paper
Language
English
Original Abstract
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.
Keywords
automatic checkout, product counting, image inpainting, object detection, object tracking
Authors
BARTL, V.; ŠPAŇHEL, J.; HEROUT, A.
Released
24. 6. 2022
Publisher
IEEE Computer Society
Location
New Orleans, LA
ISBN
2160-7516
Year of study
2022
Number
7
Pages from
3114
Pages to
3122
Pages count
9
URL
https://ieeexplore.ieee.org/document/9857198
BibTex
@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" }