People Detection in Fish-eye Top-views

Meltem Demirkus, Ling Wang, Michael Eschey, Herbert Kaestle, Fabio Galasso

2017

Abstract

Is the detection of people in top views any easier than from the much researched canonical fronto-parallel views (e.g. Caltech and INRIA pedestrian datasets)? We show that in both cases people appearance variability and false positives in the background limit performance. Additionally, we demonstrate that the use of fish-eye lenses further complicates the top-view people detection, since the person viewpoint ranges from nearly-frontal, at the periphery of the image, to perfect top-views, in the image center, where only the head and shoulder top profiles are visible. We contribute a new top-view fish-eye benchmark, we experiment with a state-of-the-art person detector (ACF) and evaluate approaches which balance less variability of appearance (grid of classifiers) with the available amount of data for training. Our results indicate the importance of data abundance over the model complexity and additionally stress the importance of an exact geometric understanding of the problem, which we also contribute here.

Paper Citation

in Harvard Style

Demirkus M., Wang L., Eschey M., Kaestle H. and Galasso F. (2017). People Detection in Fish-eye Top-views . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 141-148. DOI: 10.5220/0006094701410148

in Bibtex Style

@conference{visapp17,
author={Meltem Demirkus and Ling Wang and Michael Eschey and Herbert Kaestle and Fabio Galasso},
title={People Detection in Fish-eye Top-views},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006094701410148},
isbn={978-989-758-226-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - People Detection in Fish-eye Top-views
SN - 978-989-758-226-4
AU - Demirkus M.
AU - Wang L.
AU - Eschey M.
AU - Kaestle H.
AU - Galasso F.
PY - 2017
SP - 141
EP - 148
DO - 10.5220/0006094701410148