Bio-inspired Model for Motion Estimation using an Address-event Representation

Luma Issa Abdul-Kreem, Heiko Neumann

2015

Abstract

In this paper, we propose a new bio-inspired approach for motion estimation using a Dynamic Vision Sensor (DVS) (Lichtsteiner et al., 2008), where an event-based-temporal window accumulation is introduced. This format accumulates the activity of the pixels over a short time, i.e. several μs. The optic flow is estimated by a new neural model mechanism which is inspired by the motion pathway of the visual system and is consistent with the vision sensor functionality, where new temporal filters are proposed. Since the DVS already generates temporal derivatives of the input signal, we thus suggest a smoothing temporal filter instead of biphasic temporal filters that introduced by (Adelson and Bergen, 1985). Our model extracts motion information via a spatiotemporal energy mechanism which is oriented in the space-time domain and tuned in spatial frequency. To achieve balanced activities of individual cells against the neighborhood activities, a normalization process is carried out. We tested our model using different kinds of stimuli that were moved via translatory and rotatory motions. The results highlight an accurate flow estimation compared with synthetic ground truth. In order to show the robustness of our model, we examined the model by probing it with synthetically generated ground truth stimuli and realistic complex motions, e.g. biological motions and a bouncing ball, with satisfactory results.

Paper Citation

in Harvard Style

Abdul-Kreem L. and Neumann H. (2015). Bio-inspired Model for Motion Estimation using an Address-event Representation . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 335-346. DOI: 10.5220/0005311503350346

in Bibtex Style

@conference{visapp15,
author={Luma Issa Abdul-Kreem and Heiko Neumann},
title={Bio-inspired Model for Motion Estimation using an Address-event Representation},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={335-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005311503350346},
isbn={978-989-758-091-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - Bio-inspired Model for Motion Estimation using an Address-event Representation
SN - 978-989-758-091-8
AU - Abdul-Kreem L.
AU - Neumann H.
PY - 2015
SP - 335
EP - 346
DO - 10.5220/0005311503350346