Particle filter with ball size adaptive tracking window and ball feature likelihood model for ball’s 3D position tracking in volleyball analysis

Xina Cheng, Xizhou Zhuang, Yuan Wang, Masaaki Honda, Takeshi Ikenaga

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

3D position tracking of the ball plays a crucial role in professional volleyball analysis. In volleyball games, the constraint conditions that limit the performance of the ball tracking include the fast irregular movement of the ball, the small-size of the ball, the complex background as well as the occlusion problem caused by players. This paper proposes a ball size adaptive (BSA) tracking window, a ball feature likelihood model and an anti-occlusion likelihood measurement (AOLM) base on Particle Filter for improving the accuracy. By adaptively changing the tracking windows according to the ball size, it is possible to track the ball with changing size in different video images. On the other hand, the ball feature likelihood enables to track stably even in complex background. Furthermore, AOLM based on a multiple-camera system solves the occlusion problems since it can eliminate the low likelihood caused by occlusion. Experimental results which are based on the HDTV video sequences (2014 Inter High School Games of Men’s Volleyball) captured by four cameras located at the corners of the court show that the success rate of the ball’s 3D position tracking achieves 93.39 %.

Original languageEnglish
Pages (from-to)203-211
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9314
DOIs
Publication statusPublished - 2015

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Keywords

  • 3D position tracking
  • Ball feature
  • Likelihood model
  • Particle filter
  • Volleyball analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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