About this paper

Appears in:
Pages: 4380-4382
Publication year: 2017
ISBN: 978-84-697-6957-7
ISSN: 2340-1095
doi: 10.21125/iceri.2017.1168

Conference name: 10th annual International Conference of Education, Research and Innovation
Dates: 16-18 November, 2017
Location: Seville, Spain

3D POSE ESTIMATION METHOD FOR ASSISTING SPORTS TEACHING

K. Tanaka

Kindai University (JAPAN)
When sports teachers induce their students to understand proper forms in target sports, higher effect can be obtained if video images of poses of the students themselves are presented for their observation. A viewpoint change function is desirable when a learner observes the forms using video images. Recently, TV sports programs have been employing multiple-view camera systems, which has facilitated smooth switching of viewpoint in TV programs. On the other hand, bringing multiple cameras into an ordinary gymnasium is difficult due to its cost and difficulties in operation. The objective of this research is to provide an application software that generates a 3D human model of a player (i.e., a virtual player) using a single video camera for sports teachers, thereby enabling observation of the virtual player's poses from any point of view. The recent availability of RGB-D cameras (e.g., Kinect by Microsoft Corp.), which are less expensive and readily available, has facilitated 3D motion capture applications from a single view. However, depth sensors in the RGB-D cameras have strict limitations on distance measurement. Therefore, this study has been developing a method that estimates 3D poses of players in 2D images employing an ordinary single video camera. For the estimation, the method utilizes geometric constraints on the field of play (e.g. tennis court) and geometric constraints on the line of sight from camera to player's joint. As a first step, the study focused on karate teaching and developed a semiautomatic method for the estimation. Joints on the 2D image are specified manually. Recent studies on joint detection proposed a detection method based on machine learning. The second step in this study takes advantage of that detection method. This paper describes the method developed in the first step.
@InProceedings{TANAKA20173DP,
author = {Tanaka, K.},
title = {3D POSE ESTIMATION METHOD FOR ASSISTING SPORTS TEACHING},
series = {10th annual International Conference of Education, Research and Innovation},
booktitle = {ICERI2017 Proceedings},
isbn = {978-84-697-6957-7},
issn = {2340-1095},
doi = {10.21125/iceri.2017.1168},
url = {http://dx.doi.org/10.21125/iceri.2017.1168},
publisher = {IATED},
location = {Seville, Spain},
month = {16-18 November, 2017},
year = {2017},
pages = {4380-4382}}
TY - CONF
AU - K. Tanaka
TI - 3D POSE ESTIMATION METHOD FOR ASSISTING SPORTS TEACHING
SN - 978-84-697-6957-7/2340-1095
DO - 10.21125/iceri.2017.1168
PY - 2017
Y1 - 16-18 November, 2017
CI - Seville, Spain
JO - 10th annual International Conference of Education, Research and Innovation
JA - ICERI2017 Proceedings
SP - 4380
EP - 4382
ER -
K. Tanaka (2017) 3D POSE ESTIMATION METHOD FOR ASSISTING SPORTS TEACHING, ICERI2017 Proceedings, pp. 4380-4382.
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