Recognition of sports action pattern based on sparse matrix vector
- https://doi.org/10.2991/icmmita-16.2016.281How to use a DOI?
- recognition of sport action; sparse matrix vector; optical-flow features; pattern recognition
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/icmmita-16.2016.281 ID - Song2017/01 ER -