Open Access

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

EURASIP Journal on Advances in Signal Processing20082008:460913

Received: 17 January 2008

Accepted: 14 July 2008

Published: 6 August 2008


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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Authors’ Affiliations

Department of Electrical Engineering, National Cheng Kung University
Department of Electrical and Computer Engineering, University of Wisconsin Madison


© Chen-Yu Chen et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.