ShapeAug: Occlusion Augmentation for Event Camera Data

Katharina Bendig, René Schuster, René Schuster, Didier Stricker, Didier Stricker

2024

Abstract

Recently, Dynamic Vision Sensors (DVSs) sparked a lot of interest due to their inherent advantages over conventional RGB cameras. These advantages include a low latency, a high dynamic range and a low energy consumption. Nevertheless, the processing of DVS data using Deep Learning (DL) methods remains a challenge, particularly since the availability of event training data is still limited. This leads to a need for event data augmentation techniques in order to improve accuracy as well as to avoid over-fitting on the training data. Another challenge especially in real world automotive applications is occlusion, meaning one object is hindering the view onto the object behind it. In this paper, we present a novel event data augmentation approach, which addresses this problem by introducing synthetic events for randomly moving objects in a scene. We test our method on multiple DVS classification datasets, resulting in an relative improvement of up to 6.5 % in top1-accuracy. Moreover, we apply our augmentation technique on the real world Gen1 Automotive Event Dataset for object detection, where we especially improve the detection of pedestrians by up to 5 %.

Paper Citation

in Harvard Style

Bendig K., Schuster R. and Stricker D. (2024). ShapeAug: Occlusion Augmentation for Event Camera Data. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 352-359. DOI: 10.5220/0012393500003654

in Bibtex Style

@conference{icpram24,
author={Katharina Bendig and René Schuster and Didier Stricker},
title={ShapeAug: Occlusion Augmentation for Event Camera Data},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={352-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012393500003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - ShapeAug: Occlusion Augmentation for Event Camera Data
SN - 978-989-758-684-2
AU - Bendig K.
AU - Schuster R.
AU - Stricker D.
PY - 2024
SP - 352
EP - 359
DO - 10.5220/0012393500003654
PB - SciTePress