Robust Head-shoulder Detection using Deformable Part-based Models

Enes Dayangac, Christian Wiede, Julia Richter, Gangolf Hirtz

2015

Abstract

Conventional person detection algorithms lack of robustness, especially when the person is partially occluded. We propose thereby a robust head-shoulder detector in 2-D images using deformable part-based models. This detector can be used in a variety of applications such as people counting and person dwell time measurements. In experiments, we compare the head-shoulder detector with the full body detector quantitatively and analyze the robustness of the detector in realistic scenarios. In the results, we show that the model learned with our method outperforms other methods proposed in related work on an ambient assisted living application.

Paper Citation

in Harvard Style

Dayangac E., Wiede C., Richter J. and Hirtz G. (2015). Robust Head-shoulder Detection using Deformable Part-based Models . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 236-243. DOI: 10.5220/0005266002360243

in Bibtex Style

@conference{visapp15,
author={Enes Dayangac and Christian Wiede and Julia Richter and Gangolf Hirtz},
title={Robust Head-shoulder Detection using Deformable Part-based Models},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={236-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005266002360243},
isbn={978-989-758-090-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Robust Head-shoulder Detection using Deformable Part-based Models
SN - 978-989-758-090-1
AU - Dayangac E.
AU - Wiede C.
AU - Richter J.
AU - Hirtz G.
PY - 2015
SP - 236
EP - 243
DO - 10.5220/0005266002360243