Multi-Camera 3D Pedestrian Tracking Using Graph Neural Networks

Isabella de Andrade, João Lima, João Lima

2023

Abstract

Tracking the position of pedestrians over time through camera images is a rising computer vision research topic. In multi-camera settings, the researches are even more recent. Many solutions use supervised neural networks to solve this problem, requiring much effort to annotate the data and time spent training the network. This work aims to develop variations of pedestrian tracking algorithms, avoid the need to have annotated data and compare the results obtained through accuracy metrics. Therefore, this work proposes an approach for tracking pedestrians in 3D space in multi-camera environments using the Message Passing Neural Network framework inspired by graphs. We evaluated the solution using the WILDTRACK dataset and a generalizable detection method, reaching 77.1% of MOTA when training with data obtained by a generalizable tracking algorithm, similar to current state-of-the-art accuracy. However, our algorithm can track the pedestrians at a rate of 40 fps, excluding the detection time, which is twice the most accurate competing solution.

Paper Citation

in Harvard Style

de Andrade I. and Lima J. (2023). Multi-Camera 3D Pedestrian Tracking Using Graph Neural Networks. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 974-981. DOI: 10.5220/0011674700003417

in Bibtex Style

@conference{visapp23,
author={Isabella de Andrade and João Lima},
title={Multi-Camera 3D Pedestrian Tracking Using Graph Neural Networks},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={974-981},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011674700003417},
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 5: VISAPP
TI - Multi-Camera 3D Pedestrian Tracking Using Graph Neural Networks
SN - 978-989-758-634-7
AU - de Andrade I.
AU - Lima J.
PY - 2023
SP - 974
EP - 981
DO - 10.5220/0011674700003417
PB - SciTePress