Motion state detection based prediction model for body parts tracking of volleyball players

Fanglu Xie, Xina Cheng, Takeshi Ikenaga

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Among sports analysis, tracking of athletes’ body parts becomes a popular theme. Marking positions of body parts on the videos which contributes to TV contents and concrete motion capture of athletes which helps promotion of sports technology make sports analysis a commercially-viable research theme. This paper proposes motion state detection based prediction model to predict the near future motions of players’ arms, band-width sobel likelihood model to observe the shape of human body parts and cluster scoring based estimation to avoid huge error. The motion state detection based prediction model can realize the tracking of players’ high-speed and random motions without templates. The band-width sobel likelihood model can fully express unique shape features of target player’s body parts. And the cluster scoring based estimation utilizes k-means cluster method to divide particle into 3 clusters and evaluate each cluster by scoring in order to prevent huge error from similar noises. The experiments are based on videos of the Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo. The tracking success rate reached over 97% for lower body and over 80% for upper body, achieving average 64% improvement of hands compared to conventional work [1].

本文言語English
ホスト出版物のタイトルAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
編集者Bing Zeng, Hongliang Li, Abdulmotaleb El Saddik, Xiaopeng Fan, Shuqiang Jiang, Qingming Huang
出版社Springer Verlag
ページ280-289
ページ数10
ISBN(印刷版)9783319773797
DOI
出版ステータスPublished - 2018
イベント18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
継続期間: 2017 9 282017 9 29

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10735 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other18th Pacific-Rim Conference on Multimedia, PCM 2017
CountryChina
CityHarbin
Period17/9/2817/9/29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

フィンガープリント 「Motion state detection based prediction model for body parts tracking of volleyball players」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル