Tracking the Evolution of a Tennis Match Using Hidden Markov Models
The creation of a cognitive perception systems capable of inferring higher-level semantic information from low-level feature and event information for a given type of multimedia content is a problem that has attracted many researchers’ attention in recent years. In this work, we address the problem of automatic interpretation and evolution tracking of a tennis match using standard broadcast video sequences as input data. The use of a hierarchical structure consisting of Hidden Markov Models is proposed. This will take low-level events as its input, and will produce an output where the final state will indicate if the point is to be awarded to one player or another. Using hand-annotated data as input for the classifier described, we have witnessed 100% of the points correctly awarded to the players.
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