Motion Entropy Feature and Its Applications to Event-Based Segmentation of Sports Video
© Chen-Yu Chen et al. 2008
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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