AutoPOSE: Large-scale Automotive Driver Head Pose and Gaze Dataset with Deep Head Orientation Baseline

Mohamed Selim, Ahmet Firintepe, Alain Pagani, Didier Stricker

2020

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.

Paper Citation

in Harvard Style

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 (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 599-606. DOI: 10.5220/0009330105990606

in Bibtex Style

@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 (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={599-606},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009330105990606},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - 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
PB - SciTePress