Volume-based Human Re-identification with RGB-D Cameras

Serhan Cosar, Claudio Coppola, Nicola Bellotto

Abstract

This paper presents an RGB-D based human re-identification approach using novel biometrics features from the body’s volume. Existing work based on RGB images or skeleton features have some limitations for real-world robotic applications, most notably in dealing with occlusions and orientation of the user. Here, we propose novel features that allow performing re-identification when the person is facing side/backward or the person is partially occluded. The proposed approach has been tested for various scenarios including different views, occlusion and the public BIWI RGBD-ID dataset.

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Paper Citation


in Harvard Style

Cosar S., Coppola C. and Bellotto N. (2017). Volume-based Human Re-identification with RGB-D Cameras . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 389-397. DOI: 10.5220/0006155403890397


in Bibtex Style

@conference{visapp17,
author={Serhan Cosar and Claudio Coppola and Nicola Bellotto},
title={Volume-based Human Re-identification with RGB-D Cameras},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={389-397},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006155403890397},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Volume-based Human Re-identification with RGB-D Cameras
SN - 978-989-758-225-7
AU - Cosar S.
AU - Coppola C.
AU - Bellotto N.
PY - 2017
SP - 389
EP - 397
DO - 10.5220/0006155403890397