Robust Head-shoulder Detection using Deformable Part-based Models

Enes Dayangac; Christian Wiede; Julia Richter; Gangolf Hirtz

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Paper

Robust Head-shoulder Detection using Deformable Part-based Models

Topics: Machine Learning Technologies for Vision; Object and Face Recognition; Object Detection and Localization

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, 236-243, 2015 , Berlin, Germany

Authors: Enes Dayangac ; Christian Wiede ; Julia Richter and Gangolf Hirtz

Affiliation: Technische Universität Chemnitz, Germany

Keyword(s): Person Detection, Head-shoulder Detection, Ambient Assisted Living, Latent SVM, DPM, ACF-Detector.

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.

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Paper citation in several formats:

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 (VISIGRAPP 2015) - Volume 3: VISAPP; ISBN 978-989-758-090-1; ISSN 2184-4321, SciTePress, pages 236-243. DOI: 10.5220/0005266002360243

@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 (VISIGRAPP 2015) - Volume 3: VISAPP},
year={2015},
pages={236-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005266002360243},
isbn={978-989-758-090-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP
TI - Robust Head-shoulder Detection using Deformable Part-based Models
SN - 978-989-758-090-1
IS - 2184-4321
AU - Dayangac, E.
AU - Wiede, C.
AU - Richter, J.
AU - Hirtz, G.
PY - 2015
SP - 236
EP - 243
DO - 10.5220/0005266002360243
PB - SciTePress