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Motion Entropy Feature and Its Applications to Event-Based Segmentation of Sports Video

Abstract

An entropy-based criterion is proposed to characterize the pattern and intensity of object motion in a video sequence as a function of time. By applying a homoscedastic error model-based time series change point detection algorithm 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.

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Correspondence to Chen-Yu Chen.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Chen, CY., Wang, JC., Wang, JF. et al. Motion Entropy Feature and Its Applications to Event-Based Segmentation of Sports Video. EURASIP J. Adv. Signal Process. 2008, 460913 (2008). https://doi.org/10.1155/2008/460913

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  • DOI: https://doi.org/10.1155/2008/460913

Keywords

  • Video Sequence
  • Change Point
  • Object Motion
  • Full Article
  • Point Detection