ML Anthology

PersonGONE: Image Inpainting for Automated Checkout Solution

Vojtech Bartl, Jakub Spanhel, Adam Herout
CVPRW 2022 pp. 3114-3122
doi:10.1109/CVPRW56347.2022.00351 /cvprw/2022/bartl2022cvprw-persongone/

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 GitHub1.

CVPRW Semantic Scholar

Cite

Text

Bartl et al. "PersonGONE: Image Inpainting for Automated Checkout Solution." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00351

Markdown

[Bartl et al. "PersonGONE: Image Inpainting for Automated Checkout Solution." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/bartl2022cvprw-persongone/) doi:10.1109/CVPRW56347.2022.00351

BibTeX

@inproceedings{bartl2022cvprw-persongone,
  title     = {{PersonGONE: Image Inpainting for Automated Checkout Solution}},
  author    = {Bartl, Vojtech and Spanhel, Jakub and Herout, Adam},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2022},
  pages     = {3114-3122},
  doi       = {10.1109/CVPRW56347.2022.00351},
  url       = {https://mlanthology.org/cvprw/2022/bartl2022cvprw-persongone/}
}