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Bayesian event detection for sport games with hidden Markov model

Authors:

Tomohiro Yazaki,

Takashi Matsumoto,

Nobuyuki YagiAuthors Info & Claims

Pattern Analysis & Applications, Volume 15, Issue 1

Pages 59 - 72

Published: 01 February 2012 Publication History

Abstract

Event detection can be defined as the problem of detecting when a target event has occurred, from a given data sequence. Such an event detection problem can be found in many fields in science and engineering, such as signal processing, pattern recognition, and image processing. In recent years, many data sequences used in these fields, especially in video data analysis, tend to be high dimensional. In this paper, we propose a novel event detection method for high-dimensional data sequences in soccer video analysis. The proposed method assumes a Bayesian hidden Markov model with hyperparameter learning in addition to the parameter leaning. This is in an attempt to reduce undesired influences from ineffective components within the high-dimensional data. Implemention is performed by Markov Chain Monte Carlo. The proposed method was tested against an event detection problem with sequences of 40-dimensional feature values extracted from real professional soccer games. The algorithm appears functional.

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Cited By

Mimura TNakada Y(2019)Quantification of Pass Plays Based on Geometric Features of Formations in Team SportsProceedings of the 10th International Symposium on Information and Communication Technology10.1145/3368926.3369673(306-313)Online publication date: 4-Dec-2019
https://dl.acm.org/doi/10.1145/3368926.3369673

Bayesian event detection for sport games with hidden Markov model
1. Mathematics of computing
  1. Probability and statistics

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Information & Contributors

Information

Published In

cover image Pattern Analysis & Applications

Pattern Analysis & Applications Volume 15, Issue 1

February 2012

50 pages

ISSN:1433-7541

EISSN:1433-755X

Issue’s Table of Contents

Copyright © Copyright © 2012 Springer-Verlag London Limited.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2012

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Cited By

Mimura TNakada Y(2019)Quantification of Pass Plays Based on Geometric Features of Formations in Team SportsProceedings of the 10th International Symposium on Information and Communication Technology10.1145/3368926.3369673(306-313)Online publication date: 4-Dec-2019
https://dl.acm.org/doi/10.1145/3368926.3369673

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