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Authors: Mohamed Selim 1 ; Ahmet Firintepe 2 ; Alain Pagani 1 and Didier Stricker 1

Affiliations: 1 German Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, Kaiserslautern, Germany ; 2 BMW Group, Munich, Germany

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Driving, Head Pose Estimation, Deep Learning, Infrared Camera, Kinect V2, Eye Gaze.

Abstract: In computer vision research, public datasets are crucial to objectively assess new algorithms. By the wide use of deep learning methods to solve computer vision problems, large-scale datasets are indispensable for proper network training. Various driver-centered analysis depend on accurate head pose and gaze estimation. In this paper, we present a new large-scale dataset, AutoPOSE. The dataset provides ∼ 1.1M IR images taken from the dashboard view, and ∼ 315K from Kinect v2 (RGB, IR, Depth) taken from center mirror view. AutoPOSE’s ground truth -head orientation and position-was acquired with a sub-millimeter accurate motion capturing system. Moreover, we present a head orientation estimation baseline with a state-of-the-art method on our AutoPOSE dataset. We provide the dataset as a downloadable package from a public website.

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Paper citation in several formats:
Selim, M.; Firintepe, A.; Pagani, A. and Stricker, D. (2020). AutoPOSE: Large-scale Automotive Driver Head Pose and Gaze Dataset with Deep Head Orientation Baseline.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2, ISSN 2184-4321, pages 599-606. DOI: 10.5220/0009330105990606

@conference{visapp20,
author={Mohamed Selim. and Ahmet Firintepe. and Alain Pagani. and Didier Stricker.},
title={AutoPOSE: Large-scale Automotive Driver Head Pose and Gaze Dataset with Deep Head Orientation Baseline},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={599-606},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009330105990606},
isbn={978-989-758-402-2},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - AutoPOSE: Large-scale Automotive Driver Head Pose and Gaze Dataset with Deep Head Orientation Baseline
SN - 978-989-758-402-2
AU - Selim, M.
AU - Firintepe, A.
AU - Pagani, A.
AU - Stricker, D.
PY - 2020
SP - 599
EP - 606
DO - 10.5220/0009330105990606

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