Journal of Robotics, Networking and Artificial Life

Volume 4, Issue 1, June 2017, Pages 5 - 9

Action recognition based on binocular vision

Authors
Yiwei Ru, Hongyue Du, Shuxiao Li, Hongxing Chang
Corresponding Author
Yiwei Ru
Available Online 1 June 2017.
DOI
10.2991/jrnal.2017.4.1.2How to use a DOI?
Keywords
action recognition; binocular version; convolutional neural networks; motion history image;
Abstract

Aimed at the problem that the recognition accuracy of the monocular camera is low, we propose a binocular vision recognition algorithm for action recognition based on HART-Net(Human action recognition networks).Firstly, the left and right views obtained by the binocular camera are matched to obtain the depth map of the human body. Then, the depth information is projected onto the three planes, the projection images of three directions are used to construct MHI (motion history image), and are combined into a new image. Finally, we use HART-Net to train a classifier for action recognition. Experimental results show that the binocular recognition algorithm is 18% more accurate than the monocular recognition algorithm.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
4 - 1
Pages
5 - 9
Publication Date
2017/06/01
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.2017.4.1.2How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Yiwei Ru
AU  - Hongyue Du
AU  - Shuxiao Li
AU  - Hongxing Chang
PY  - 2017
DA  - 2017/06/01
TI  - Action recognition based on binocular vision
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 5
EP  - 9
VL  - 4
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.1.2
DO  - 10.2991/jrnal.2017.4.1.2
ID  - Ru2017
ER  -