Recognition of sports action pattern based on sparse matrix vector

DOI

10.2991/icmmita-16.2016.281How to use a DOI?

Keywords

recognition of sport action; sparse matrix vector; optical-flow features; pattern recognition

Abstract

The Thesis proposes an algorithm of physical gesture recognition of human body based on composite features, which combines the feature of contour vector distance from the contour mass center to the edge point with the optical-flow features, and then the composite features are formed to make action recognition. According to the recognition results in the third part, we can see that such composite features achieve more than 95% correct recognition rate in the Weizmann database and KTH database, which proves the validity and feasibility of such algorithm sufficiently.

Copyright

© 2017, 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/).

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TY  - CONF
AU  - Yupeng Song
AU  - Ling Jia
PY  - 2017/01
DA  - 2017/01
TI  - Recognition of sports action pattern based on sparse matrix vector
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 1225
EP  - 1230
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.281
DO  - 10.2991/icmmita-16.2016.281
ID  - Song2017/01
ER  -