Sports video summarization based on salient motion entropy and information analysis

Bo Wei Chen, Jhing Fa Wang, Jia Ching Wang, Chen Yu Chen

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

1 Scopus citations


In this study, we presented a novel summarization method for generating sports video abstracts, which utilized motion entropy analysis and mutual information. Both of them are based on an attentive model. In order to capture and detect significant segments among a video, we exploited saliency maps by calculating color contrast, intensity contrast, and orientation contrast of frames. In the next step, motion vectors between maps were computed and converted into salient motion entropy. Meanwhile, a new algorithm based on mutual information was proposed to improve the smoothness problem when we selected boundaries of segments. The experiments showed that our proposed algorithm could not only detect highlights effectively but also generate smooth playable clips. Compared with the traditional approaches, our system improved the precision by 7.6% and enhanced smoothness by 1.2, which also verified feasibility of our system.

Original languageEnglish
Title of host publicationAdvances in Neural Network Research and Applications
Number of pages8
StatePublished - 2010
Event7th International Symposium on Neural Networks, ISNN 2010 - Shanghai, China
Duration: 6 Jun 20109 Jun 2010

Publication series

NameLecture Notes in Electrical Engineering
Volume67 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference7th International Symposium on Neural Networks, ISNN 2010


  • Information analysis
  • Motion entropy analysis
  • Salient motion entropy
  • Video summarization


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