loading
Documents

Research.Publish.Connect.

Paper

Authors: Marko Linna 1 ; Juho Kannala 2 and Esa Rahtu 3

Affiliations: 1 University of Oulu, Finland ; 2 Aalto University, Finland ; 3 Tampere University of Technology, Finland

ISBN: 978-989-758-290-5

Keyword(s): Human Pose Estimation, Person Detection, Convolutional Neural Networks.

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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.198.103.13

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - Volume 5: VISAPP, ISBN 978-989-758-290-5, pages 335-342. DOI: 10.5220/0006624403350342

@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 - Volume 5: VISAPP,},
year={2018},
pages={335-342},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006624403350342},
isbn={978-989-758-290-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - 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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.