Baseball event semantic exploring system using HMM

W. J. Tsai*, Hua Tsung Chen, Hui Zhen Gu, Suh-Yin Lee, Jen Yu Yu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Despite a lot of research efforts in baseball video processing in recent years, little work has been done in analyzing the detailed semantic baseball event detection. This paper presents an effective and efficient baseball event classification system for broadcast baseball videos. Utilizing the specifications of the baseball field and the regularity of shot transition, the system recognizes highlight in video clips and identifies what semantic baseball event of the baseball clips is currently proceeding. First, a video is segmented into several highlights starting with a PC (Pitcher and Catcher) shot and ending up with some specific shots. Before every baseball event classifier is designed, several novel schemes including some specific features such as soil percentage and objects extraction such as first base are applied. The extracted mid-level cues are used to develop baseball event classifiers based on an HMM (Hidden Markov model). Due to specific features detection, more hitting baseball events are detected and the simulation results show that the classification of twelve significant baseball events is very promising.

Original languageEnglish
Title of host publication17th International Multimedia Modeling Conference, MMM 2011
Number of pages11
StatePublished - 26 Jan 2011
Event17th Multimedia Modeling Conference, MMM 2011 - Taipei, Taiwan
Duration: 5 Jan 20117 Jan 2011


Conference17th Multimedia Modeling Conference, MMM 2011


  • Classification step
  • Hidden Markov Model
  • Hitting baseball event
  • Object detection
  • Play region classification
  • Semantic event
  • Training step


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