Hierarchical Markov model: Difference between revisions

Revision as of 09:27, 20 December 2011

Revision as of 09:28, 20 December 2011

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{{AFC submission|d|essay|ts=20111217011449|u=Nekabadesvetlina|ns=5}} <!-- Please leave this line alone! -->

*{{afc comment|1=See [[WP:TONE]], [[WP:NOR]], [[WP:SYNTH]]. <small><span style="border:1px solid;background:#00008B">[[User:Chzz|'''<span style="background:#00008B;color:white">&nbsp;Chzz&nbsp;</span>''']][[User talk:Chzz|<span style="color:#00008B;background-color:yellow;">&nbsp;►&nbsp;</span>]]</span></small> 09:28, 20 December 2011 (UTC)}}

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Hierarchical Markov Models have been recently applied to recognize human behavior at different levels of abstraction. The term ''behavior recognition'' is used to refer to the task of determining a high-level activity that a person is performing (e.g., cooking) based on a sequence of low-level observations (e.g., the ___location of the person in a room) often captured by devices such as video cameras and motion sensors. This article briefly introduces two kinds of Hierarchical Markov Models--[[Hierarchical hidden Markov model|Hierarchical Hidden Markov Models]] and Abstract Hidden Markov Models--and then discusses how they have been used for behavior recognition.

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[[:Category:Machine_learning]]

[[:Category:Artificial_intelligence]]