Ice hockey shooting event modeling with mixture hidden Markov model

Authors:
Xiaofeng Wang;Xiao-Ping Zhang
Affiliations:
Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada;Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada
Venue:
Multimedia Tools and Applications
Year:
2012

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Abstract

In this paper we present a new event analysis framework based on mixture hidden Markov model (HMM) for ice hockey videos. Hockey is a competitive sport and hockey videos are hard to analyze because of the homogeneity of its frame features. However, the temporal dynamics of hockey videos is highly structured. Using the mixture representation of local observations and Markov chain property of hockey event structure, we successfully model the hockey event as a mixture HMM. Based on the mixture HMM, the hockey event could be classified with high accuracy. Two types of mixture HMMs, Gaussian mixture and independent component analysis (ICA) mixture, are compared for the hockey video event classification. The results confirm our analysis that the mixture HMM is a suitable model to deal with videos with intensive activities. The new mixture HMM hockey event model could be a very useful tool for hockey game analysis.