Title
Tracking the Evolution of a Tennis Match Using Hidden Markov Models
Abstract
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.
Year
DOI
Venue
2004
10.1007/978-3-540-27868-9_119
Lecture Notes in Computer Science
Keywords
Field
DocType
hidden markov model
Broadcasting,Computer science,Semantic information,Artificial intelligence,Classifier (linguistics),Hidden Markov model,Image sequence,Perception,Machine learning,Semantics,Statistical analysis
Conference
Volume
ISSN
Citations 
3138
0302-9743
1
PageRank 
References 
Authors
0.37
7
3
Name
Order
Citations
PageRank
Ilias Kolonias120.74
William J. Christmas269754.09
J. Kittler3143461465.03