A Hierarchical Bayesian Hidden Markov Model for Multi-Dimensional Discrete Data

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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“…In the hierarchical Bayesian HMM [8,9], the reparameterization of the hyperparameter vector b f l ;i is described by the following equation:…”

Section: Reparameterization Of B F L ;Imentioning

confidence: 99%

“…This paper attempts to perform soccer game event detection for constructing metadata creating system using the hierarchical Bayesian Hidden Markov Model (HMM) [8,9] where performance evaluation was not conducted. ''Hierarchical'' in the present context refers to a learning scheme for hyperparameters behind HMM parameters.…”

Section: Purposementioning

confidence: 99%