Multi-channel segmental hidden markov models for sports video mining

Authors:
Yi Ding;Guoliang Fan
Affiliations:
Oklahoma State University, Stillwater, OK, USA;Oklahoma State University, Stillwater, OK, USA
Venue:
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Year:
2008

Citing 5
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Abstract

We present a new multi-channel segmental hidden Markov model (MCSHMM) for sports video mining that is a unique probabilistic graphical model with two advantages. One is the integration of both hierarchical and parallel structures that offer more flexibility and capacity of capturing the interaction between multiple Markov chains. The other is the incorporation of the segmental HMM that represents a variable-length sequence of observations. Especially, we develop a maximum a posteriori (MAP) estimator to optimize model structures and model parameters simultaneously. The proposed MCSHMM is used for American football video analysis, where two semantics structures, play types and camera views, are involved. The experiment shows that the MCSHMM outperforms previous HMM-based approaches.