Imperial College London
Alternate

ProfessorChristos-SavvasBouganis

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Intelligent Digital Systems
 
 
 
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Contact

 

+44 (0)20 7594 6144christos-savvas.bouganis Website

 
 
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Location

 

904Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Vasileiadis:2019:10.1016/j.cviu.2019.04.011,
author = {Vasileiadis, M and Bouganis, C-S and Tzovaras, D},
doi = {10.1016/j.cviu.2019.04.011},
journal = {Computer Vision and Image Understanding},
pages = {12--23},
title = {Multi-person 3D pose estimation from 3D cloud data using 3D convolutional neural networks},
url = {http://dx.doi.org/10.1016/j.cviu.2019.04.011},
volume = {185},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Human pose estimation is considered one of the major challenges in the field of Computer Vision, playing an integral role in a large variety of technology domains. While, in the last few years, there has been an increased number of research approaches towards CNN-based 2D human pose estimation from RGB images, respective work on CNN-based 3D human pose estimation from depth/3D data has been rather limited, with current approaches failing to outperform earlier methods, partially due to the utilization of depth maps as simple 2D single-channel images, instead of an actual 3D world representation. In order to overcome this limitation, and taking into consideration recent advances in 3D detection tasks of similar nature, we propose a novel fully-convolutional, detection-based 3D-CNN architecture for 3D human pose estimation from 3D data. The architecture follows the sequential network architecture paradigm, generating per-voxel likelihood maps for each human joint, from a 3D voxel-grid input, and is extended, through a bottom-up approach, towards multi-person 3D pose estimation, allowing the algorithm to simultaneously estimate multiple human poses, without its runtime complexity being affected by the number of people within the scene. The proposed multi-person architecture, which is the first within the scope of 3D human pose estimation, is comparatively evaluated on three single person public datasets, achieving state-of-the-art performance, as well as on a public multi-person dataset achieving high recognition accuracy.
AU  - Vasileiadis,M
AU  - Bouganis,C-S
AU  - Tzovaras,D
DO  - 10.1016/j.cviu.2019.04.011
EP  - 23
PY  - 2019///
SN  - 1077-3142
SP  - 12
TI  - Multi-person 3D pose estimation from 3D cloud data using 3D convolutional neural networks
T2  - Computer Vision and Image Understanding
UR  - http://dx.doi.org/10.1016/j.cviu.2019.04.011
UR  - http://hdl.handle.net/10044/1/70442
VL  - 185
ER  -
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