Motion entropy feature and its applications to event-based segmentation of sports video

Authors:
Chen-Yu Chen;Jia-Ching Wang;Jhing-Fa Wang;Yu-Hen Hu
Affiliations:
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, WI
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2008

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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.