Meta-parameters Exploration for Unsupervised Event-based Motion Analysis

Veís Oudjail, Jean Martinet

Abstract

Being able to estimate motion features is an essential step in dynamic scene analysis. Optical flow typically quantifies the apparent motion of objects. Motion features can benefit from bio-inspired models of mammalian retina, where ganglion cells show preferences to global patterns of direction, especially in the four cardinal translatory directions. We study the meta-parameters of a bio-inspired motion estimation model using event cameras, that are bio-inspired vision sensors that naturally capture the dynamics of a scene. The motion estimation model is made of an elementary Spiking Neural Network, that learns the motion dynamics in a non-supervised way through the Spike-Timing-Dependent Plasticity. After short simulation times, the model can successfully estimate directions without supervision. Some of the advantages of such networks are the non-supervised and continuous learning capabilities, and also their implementability on very low-power hardware. The model is tuned using a synthetic dataset generated for parameter estimation, made of various patterns moving in several directions. The parameter exploration shows that attention should be given to model tuning, and yet the model is generally stable over meta-parameter changes.

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Paper Citation


in Harvard Style

Oudjail V. and Martinet J. (2020). Meta-parameters Exploration for Unsupervised Event-based Motion Analysis.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2, pages 853-860. DOI: 10.5220/0009324908530860


in Bibtex Style

@conference{visapp20,
author={Veís Oudjail and Jean Martinet},
title={Meta-parameters Exploration for Unsupervised Event-based Motion Analysis},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={853-860},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009324908530860},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Meta-parameters Exploration for Unsupervised Event-based Motion Analysis
SN - 978-989-758-402-2
AU - Oudjail V.
AU - Martinet J.
PY - 2020
SP - 853
EP - 860
DO - 10.5220/0009324908530860