Ball-like observation model and multi-peak distribution estimation based particle filter for 3D Ping-pong ball tracking

Ziwei Deng, Xina Cheng, Takeshi Ikenaga

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

3 Citations (Scopus)

Abstract

3D ball tracking is of great significance to ping-pong game analysis, which can be utilized to applications such as TV content and tactic analysis. To achieve a high success rate in ping-pong ball tracking, the main problems are the lack of unique features and the complexity of background, which make it difficult to distinguish the ball from similar noises. This paper proposes a ball-like observation model and a multi-peak distribution estimation to improve accuracy. For the balllike observation model, we utilize gradient feature from the edge of upper semicircle to construct a histogram, besides, ball-size likelihood is proposed to deal with the situation when noises are different in size with the ball. The multi-peak distribution estimation aims at obtaining a precise ball position in case the partidles' weight distribution has multiple peaks. Experiments are based on ping-pong videos recorded in an official match from 4 perspectives, which in total have 122 hit cases with 2 pairs of players. The tracking success rate finally reaches 99.33%.

Original languageEnglish
Title of host publicationProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages390-393
Number of pages4
ISBN (Electronic)9784901122160
DOIs
Publication statusPublished - 2017 Jul 19
Event15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
Duration: 2017 May 82017 May 12

Other

Other15th IAPR International Conference on Machine Vision Applications, MVA 2017
CountryJapan
CityNagoya
Period17/5/817/5/12

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Deng, Z., Cheng, X., & Ikenaga, T. (2017). Ball-like observation model and multi-peak distribution estimation based particle filter for 3D Ping-pong ball tracking. In Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 (pp. 390-393). [7986883] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/MVA.2017.7986883