Event-Based Semantic-Aided Motion Segmentation

Chenao Jiang, Julien Moreau, Franck Davoine

2024

Abstract

Event cameras are emerging visual sensors inspired by biological systems. They capture intensity changes asynchronously with a temporal precision of up to µs, in contrast to traditional frame imaging techniques running at a fixed frequency of tens of Hz. However, effectively utilizing the data generated by these sensors requires the development of new algorithms and processing. In light of event cameras’ significant advantages in capturing high-speed motion, researchers have turned their attention to event-based motion segmentation. Building upon (Mitrokhin et al., 2019) framework, we propose leveraging semantic segmentation enable the end-to-end network not only to segment moving objects from background motion, but also to achieve semantic segmentation of distinct moving objects. Remarkably, these capabilities are achieved while maintaining the network’s low parameter count of 2.5M. To validate the effectiveness of our approach, we conduct experiments using the EVIMO dataset and the new and more challenging EVIMO2 dataset (Burner et al., 2022). The results demonstrate improvements attained by our method, showcasing its potential in event-based multi-objects motion segmentation.

Paper Citation

in Harvard Style

Jiang C., Moreau J. and Davoine F. (2024). Event-Based Semantic-Aided Motion Segmentation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 159-171. DOI: 10.5220/0012308100003660

in Bibtex Style

@conference{visapp24,
author={Chenao Jiang and Julien Moreau and Franck Davoine},
title={Event-Based Semantic-Aided Motion Segmentation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={159-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012308100003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Event-Based Semantic-Aided Motion Segmentation
SN - 978-989-758-679-8
AU - Jiang C.
AU - Moreau J.
AU - Davoine F.
PY - 2024
SP - 159
EP - 171
DO - 10.5220/0012308100003660
PB - SciTePress