Head Detection with Depth Images in the Wild

Diego Ballotta, Guido Borghi, Roberto Vezzani, Rita Cucchiara

2018

Abstract

Head detection and localization is a demanding task and a key element for many computer vision applications, like video surveillance, Human Computer Interaction and face analysis. The stunning amount of work done for detecting faces on RGB images, together with the availability of huge face datasets, allowed to setup very effective systems on that domain. However, due to illumination issues, infrared or depth cameras may be required in real applications. In this paper, we introduce a novel method for head detection on depth images that exploits the classification ability of deep learning approaches. In addition to reduce the dependency on the external illumination, depth images implicitly embed useful information to deal with the scale of the target objects. Two public datasets have been exploited: the first one, called Pandora, is used to train a deep binary classifier with face and non-face images. The second one, collected by Cornell University, is used to perform a cross-dataset test during daily activities in unconstrained environments. Experimental results show that the proposed method overcomes the performance of state-of-art methods working on depth images.

Paper Citation

in Harvard Style

Ballotta D., Borghi G., Vezzani R. and Cucchiara R. (2018). Head Detection with Depth Images in the Wild. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 56-63. DOI: 10.5220/0006541000560063

in Bibtex Style

@conference{visapp18,
author={Diego Ballotta and Guido Borghi and Roberto Vezzani and Rita Cucchiara},
title={Head Detection with Depth Images in the Wild},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={56-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006541000560063},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - Head Detection with Depth Images in the Wild
SN - 978-989-758-290-5
AU - Ballotta D.
AU - Borghi G.
AU - Vezzani R.
AU - Cucchiara R.
PY - 2018
SP - 56
EP - 63
DO - 10.5220/0006541000560063
PB - SciTePress