Real-time Human Pose Estimation with Convolutional Neural Networks

Marko Linna, Juho Kannala, Esa Rahtu

2018

Abstract

In this paper, we present a method for real-time multi-person human pose estimation from video by utilizing convolutional neural networks. Our method is aimed for use case specific applications, where good accuracy is essential and variation of the background and poses is limited. This enables us to use a generic network architecture, which is both accurate and fast. We divide the problem into two phases: (1) pre-training and (2) finetuning. In pre-training, the network is learned with highly diverse input data from publicly available datasets, while in finetuning we train with application specific data, which we record with Kinect. Our method differs from most of the state-of-the-art methods in that we consider the whole system, including person detector, pose estimator and an automatic way to record application specific training material for finetuning. Our method is considerably faster than many of the state-of-the-art methods. Our method can be thought of as a replacement for Kinect in restricted environments. It can be used for tasks, such as gesture control, games, person tracking, action recognition and action tracking. We achieved accuracy of 96.8% (PCK@0.2) with application specific data.

Paper Citation

in Harvard Style

Linna M., Kannala J. and Rahtu E. (2018). Real-time Human Pose Estimation with Convolutional Neural Networks. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 335-342. DOI: 10.5220/0006624403350342

in Bibtex Style

@conference{visapp18,
author={Marko Linna and Juho Kannala and Esa Rahtu},
title={Real-time Human Pose Estimation with Convolutional Neural Networks},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={335-342},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006624403350342},
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 5: VISAPP
TI - Real-time Human Pose Estimation with Convolutional Neural Networks
SN - 978-989-758-290-5
AU - Linna M.
AU - Kannala J.
AU - Rahtu E.
PY - 2018
SP - 335
EP - 342
DO - 10.5220/0006624403350342
PB - SciTePress