Bayesian event detection for sport games with hidden Markov model
Bayesian event detection for sport games with hidden Markov model
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DOI：
10.1007/s10044-011-0238-6
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发表时间：
2012-02-01
影响因子：
3.9
通讯作者：
Yagi, Nobuyuki
中科院分区：
文献类型：
--
作者：
Motoi, Shigeru;Misu, Toshie;Yagi, Nobuyuki
事件检测可以被定义为从给定的数据序列中检测目标事件何时发生的问题。这样的事件检测问题在信号处理、模式识别、图像处理等科学和工程领域都有广泛的应用。近年来，许多用于这些领域的数据序列，特别是在视频数据分析中，往往是高维的。本文针对足球视频分析中的高维数据序列，提出了一种新的事件检测方法。该方法假定贝叶斯隐马尔可夫模型除了参数学习外还具有超参数学习功能。这是为了减少来自高维数据内的无效分量的不期望的影响。实现采用马尔可夫链蒙特卡罗方法。利用从实际职业足球比赛中提取的40维特征值序列，对所提出的方法进行了事件检测。该算法看起来是可行的。
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.