Motion Entropy Feature and Its Applications to Event-Based Segmentation of Sports Video. (25th August 2008)

• Record Type: Journal Article
• Authors: Chen Chen, Chen-Yu Chen-Yu; Wang Wang, Jia-Ching Jia-Ching; Wang Wang, Jhing-Fa Jhing-Fa; Hu Hu, Yu-Hen Yu-Hen
• Other Names: Liao Liao Mark Mark Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2008-08-25
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/460913
• Languages: English
• ISSNs: 1687-6172

Abstract

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.