行为识别的贝叶斯-马尔可夫组合方法

N. Carter, D. P. Young, J. Ferryman
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引用次数: 18

摘要

存在许多技术可用于行为分析和识别任务。其中常见的是贝叶斯网络和隐马尔可夫模型。尽管这些技术非常强大和发达,但它们都有重要的局限性。通过将这些技术融合在一起形成贝叶斯-马尔可夫链,可以保留这两种技术的优点,同时减少它们的局限性。贝叶斯-马尔可夫技术构成了一个通用的、灵活的框架的基础,可以用附加的特性来补充马尔可夫链。这将改善用户输出,并有助于快速开发灵活和有效的行为识别系统
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A Combined Bayesian Markovian Approach for Behaviour Recognition
Numerous techniques exist which can be used for the task of behavioural analysis and recognition. Common amongst these are Bayesian networks and hidden Markov models. Although these techniques are extremely powerful and well developed, both have important limitations. By fusing these techniques together to form Bayes-Markov chains, the advantages of both techniques can be preserved, while reducing their limitations. The Bayes-Markov technique forms the basis of a common, flexible framework for supplementing Markov chains with additional features. This results in improved user output, and aids in the rapid development of flexible and efficient behaviour recognition systems
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