Event-based segmentation of sports video using motion entropy

Chen Yu Chen, Jia Ching Wang, Jhing Fa Wang, Yu Hen Hu

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

10 Scopus citations

Abstract

An event-based segmentation method for sports videos is presented. A motion entropy criterion is employed to characterize the level of intensity of relevant object motion in individual frames of a video sequence. The resulting motion entropy curve then is approximated with a piece-wise linear model using a homoscedastic error model based time series change point detection algorithm. It is observed that interesting sports events are correlated with specific patterns of the piece-wise linear model. A set of empirically derived classification rules then is derived based on these observations. Application of these rules to the motion entropy curve leads to this motion entropy curve, one is able to segment the corresponding video sequence into individual sections, each consisting of a semantically relevant event. The proposed method is tested on six hours of sports videos including basketball, soccer and tennis. Excellent experimental results are observed.

Original languageEnglish
Title of host publicationProceedings - 9th IEEE International Symposium on Multimedia, ISM 2007
Pages107-111
Number of pages5
DOIs
StatePublished - 2007
Event9th IEEE International Symposium on Multimedia, ISM 2007 - Taichung, Taiwan
Duration: 10 Dec 200712 Dec 2007

Publication series

NameProceedings - 9th IEEE International Symposium on Multimedia, ISM 2007

Conference

Conference9th IEEE International Symposium on Multimedia, ISM 2007
Country/TerritoryTaiwan
CityTaichung
Period10/12/0712/12/07

Keywords

  • Entropy-based motion feature
  • Event detection
  • Homoscedastic error model
  • Video segmentation

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