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

Fanglu Xie*, Xina Cheng, Takeshi Ikenaga

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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].

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
EditorsBing Zeng, Hongliang Li, Abdulmotaleb El Saddik, Xiaopeng Fan, Shuqiang Jiang, Qingming Huang
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319773797
Publication statusPublished - 2018
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 2017 Sept 282017 Sept 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10735 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other18th Pacific-Rim Conference on Multimedia, PCM 2017


  • Body parts tracking
  • K-means cluster
  • Likelihood model
  • Motion state detection
  • Particle filter
  • Prediction model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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