Counting People in Crowds Using Multiple Column Neural Networks

Christian Konishi, Helio Pedrini

2023

Abstract

Crowd counting through images is a research field of great interest for its various applications, such as surveil-lance camera images monitoring, urban planning. In this work, a model (MCNN-U) based on Generative Adversarial Networks (GANs) with Wasserstein cost and Multiple Column Neural Networks (MCNNs) is proposed to obtain better estimates of the number of people. The model was evaluated using two crowd counting databases, UCF-CC-50 and ShanghaiTech. In the first database, the reduction in the mean absolute error was greater than 30%, whereas the gains in efficiency were smaller in the second database. An adaptation of the LayerCAM method was also proposed for the crowd counter network visualization.

Paper Citation

in Harvard Style

Konishi C. and Pedrini H. (2023). Counting People in Crowds Using Multiple Column Neural Networks. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 363-370. DOI: 10.5220/0011704000003417

in Bibtex Style

@conference{visapp23,
author={Christian Konishi and Helio Pedrini},
title={Counting People in Crowds Using Multiple Column Neural Networks},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={363-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011704000003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Counting People in Crowds Using Multiple Column Neural Networks
SN - 978-989-758-634-7
AU - Konishi C.
AU - Pedrini H.
PY - 2023
SP - 363
EP - 370
DO - 10.5220/0011704000003417
PB - SciTePress