Motion vector and players’ features based particle filter for volleyball players tracking in 3D space

Xizhou Zhuang, Xina Cheng, Shuyi Huang, Masaaki Honda, Takeshi Ikenaga

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Multiple players tracking plays a key role in volleyball analysis. Due to the demand of developing effective tactics for professional events, players’ 3D information like speed and trajectory is needed. Although, 3D information can solve the occlusion relation problem, complete occlusion and similar feature between players may still reduce the accuracy of tracking. Thus, this paper proposes a motion vector and players’ features based particle filter for multiple players tracking in 3D space. For the prediction part, a motion vector prediction model combined with Gaussian window model is proposed to predict player’s position after occlusion. For the likelihood estimation part, a 3D distance likelihood model is proposed to avoid error tracking between two players. Also, a number detection likelihood model is used to distinguish players. With the proposed multiple players tracking algorithm, not only occlusion relation problem can be solved, but also physical features of players in the real world can be obtained. Experiment which executed on an official volleyball match video (Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo Metropolitan Gymnasium) shows that our tracking algorithm can achieve 91.9 % and 92.6 % success rate in the first and third set.

Original languageEnglish
Pages (from-to)393-401
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

Fingerprint

Motion Vector
Particle Filter
Occlusion
Likelihood
Video Games
Trajectories
Japan
Prediction Model
Model
Trajectory
Game
Predict
Prediction
Experiments
Experiment

Keywords

  • 3D space
  • Multiple players tracking
  • Particle filter
  • Volleyball

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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title = "Motion vector and players’ features based particle filter for volleyball players tracking in 3D space",
abstract = "Multiple players tracking plays a key role in volleyball analysis. Due to the demand of developing effective tactics for professional events, players’ 3D information like speed and trajectory is needed. Although, 3D information can solve the occlusion relation problem, complete occlusion and similar feature between players may still reduce the accuracy of tracking. Thus, this paper proposes a motion vector and players’ features based particle filter for multiple players tracking in 3D space. For the prediction part, a motion vector prediction model combined with Gaussian window model is proposed to predict player’s position after occlusion. For the likelihood estimation part, a 3D distance likelihood model is proposed to avoid error tracking between two players. Also, a number detection likelihood model is used to distinguish players. With the proposed multiple players tracking algorithm, not only occlusion relation problem can be solved, but also physical features of players in the real world can be obtained. Experiment which executed on an official volleyball match video (Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo Metropolitan Gymnasium) shows that our tracking algorithm can achieve 91.9 {\%} and 92.6 {\%} success rate in the first and third set.",
keywords = "3D space, Multiple players tracking, Particle filter, Volleyball",
author = "Xizhou Zhuang and Xina Cheng and Shuyi Huang and Masaaki Honda and Takeshi Ikenaga",
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AU - Zhuang, Xizhou

AU - Cheng, Xina

AU - Huang, Shuyi

AU - Honda, Masaaki

AU - Ikenaga, Takeshi

PY - 2015

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N2 - Multiple players tracking plays a key role in volleyball analysis. Due to the demand of developing effective tactics for professional events, players’ 3D information like speed and trajectory is needed. Although, 3D information can solve the occlusion relation problem, complete occlusion and similar feature between players may still reduce the accuracy of tracking. Thus, this paper proposes a motion vector and players’ features based particle filter for multiple players tracking in 3D space. For the prediction part, a motion vector prediction model combined with Gaussian window model is proposed to predict player’s position after occlusion. For the likelihood estimation part, a 3D distance likelihood model is proposed to avoid error tracking between two players. Also, a number detection likelihood model is used to distinguish players. With the proposed multiple players tracking algorithm, not only occlusion relation problem can be solved, but also physical features of players in the real world can be obtained. Experiment which executed on an official volleyball match video (Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo Metropolitan Gymnasium) shows that our tracking algorithm can achieve 91.9 % and 92.6 % success rate in the first and third set.

AB - Multiple players tracking plays a key role in volleyball analysis. Due to the demand of developing effective tactics for professional events, players’ 3D information like speed and trajectory is needed. Although, 3D information can solve the occlusion relation problem, complete occlusion and similar feature between players may still reduce the accuracy of tracking. Thus, this paper proposes a motion vector and players’ features based particle filter for multiple players tracking in 3D space. For the prediction part, a motion vector prediction model combined with Gaussian window model is proposed to predict player’s position after occlusion. For the likelihood estimation part, a 3D distance likelihood model is proposed to avoid error tracking between two players. Also, a number detection likelihood model is used to distinguish players. With the proposed multiple players tracking algorithm, not only occlusion relation problem can be solved, but also physical features of players in the real world can be obtained. Experiment which executed on an official volleyball match video (Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo Metropolitan Gymnasium) shows that our tracking algorithm can achieve 91.9 % and 92.6 % success rate in the first and third set.

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