Share Email Print
cover

Proceedings Paper • new

Field position estimation in soccer videos using convolutional neural network-based image features
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper presents a novel estimation method of field positions in soccer videos using Convolutional Neural Network (CNN)-based image features. CNN-based features have been well known to be effective for tasks in machine learning. Therefore, the proposed method adopts CNN-based image features. By using these image features, the proposed method enables accurate estimation of soccer field positions than handcrafted features, i.e., Hough transform-based features. We show the effectiveness of our method via experiment results using actual soccer videos.

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490Y (22 March 2019); doi: 10.1117/12.2521569
Show Author Affiliations
Genki Suzuki, Hokkaido Univ. (Japan)
Sho Takahashi, Hokkaido Univ. (Japan)
Takahiro Ogawa, Hokkaido Univ. (Japan)
Miki Haseyama, Hokkaido Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

© SPIE. Terms of Use
Back to Top