This paper proposes a method to automatically set indexes which describe the content of a baseball video scene. The approach is based on the technique to patternize baseball scenes using a set of rectangles with some image features and a motion vector. Each baseball scene is expressed as a sequence of symbols based on the patternized data of every shot and given "event-indexes" (home-run, single-hit, walk, etc.) using hidden Markov models which have been trained to the sequences. The processes to construct training data and to set event-indexes to baseball scenes are described in detail. The proposed method is evaluated by an experiment using seven major league baseball video games.
Baseball video indexing using patternization of scenes and hidden Markov model
IEEE International Conference on Image Processing 2005 ; 3 ; III-1212
2005-01-01
437913 byte
Conference paper
Electronic Resource
English
Baseball Video Indexing using Patternization of Scenes and Hidden Markov Model
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