Hierarchical Markov model: Difference between revisions

Revision as of 09:28, 20 December 2011

Revision as of 06:14, 26 September 2013

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{{AFC submission|d|essay|ts=20111217011449|u=Nekabadesvetlina|ns=5}}

*{{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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=== Abstract Hidden Markov Model ===

An Abstract Hidden Markov Model (AHMM) <ref name="AHMM">H. H. Bui, S. Venkatesh, and G. West [http://dl.acm.org/citation.cfm?id=1622824 Policy recognition in the abstract hidden markov model]. Journal of Artificial Intelligence Research, vol. 17, p. 451–499, 2002.</ref> is an extension of a [[Hierarchical hidden Markov model|Hierarchical Hidden Markov Model]] that allows the choice of how a high-level activity (policy) will be decomposed into a sequence of lower-level activities (policies) to be dependent on the current state of the environment. Thus, simple types of context-sensitive behaviors can be captured by an AHMM .<ref name="RecognitionOfHumanActivityThroughHierarchicalStochasticLearning"> S. Luhr, H. H. Bui, S. Venkatesh, and G. A. W. West [http://dl.acm.org/citation.cfm?id=826390 Recognition of Human Activity through Hierarchical Stochastic Learning]. PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. </ref>.

== Applications to Human Behavior Recognition ==

[[Image:Kitchen.png|right|thumb|600px|

Figure 1. Layout for kitchen. (a) the “food preparation first” and (b) the “washing dishes first” meal preparation sequences. <br>

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'''Hierarchical Hidden Markov Models (HHMMs)''' have been used in the context of eldercare aiming to identify behaviors such as eating dinner, watching television, and cooking <ref name="RecognitionOfHumanActivityThroughHierarchicalStochasticLearning" />. The goal is to learn the pattern of executing such activities normally, so that when an abnormal execution occurs (e.g., an elder person falls down while cooking), it can be automatically and instantaneously detected. Then, the appropriate action (e.g., provide audio/video cues to help the elderly or notify the appropriate caretaker that the elderly needs help) can then be taken before negative consequences for the elder person's health occur.

[[Supervised learning]] (a class of [[machine learning]] techniques) was used in <ref name="RecognitionOfHumanActivityThroughHierarchicalStochasticLearning" /> to learn an HHMM for four simplistic ways for dinner preparation ("spending some time preparing the food and rummaging through the fridge before the meal is cooked", "washing dishes prior to cooking on the stove", "going to the sink to wash the dishes then spending time at the food preparation area and the fridge before finally cooking the meal", "transition between each area of interest in a round robin fashion, starting and ending at the stove, before leaving the room") and five typical living room activities ("watch television", "read a book on the couch", "eat dinner", "eat dinner while watching television" and "there is nothing good on TV, read a book instead"). The person's position in the room was captured at short intervals of time and a sequence of observations was thus created. Observation sequences were labelled with the corresponding behavior and were used to learn HHMMs for the high-level dinner preparation and living room behaviors. Two example kitchen behaviors are shown in Figure 1. Figure 2 shows an HHMM learned for the "food preparation first" kitchen behavior.

[[Image:LearnedHHMM.png|center|thumb|800px|

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== Advantages of Hierarchical Markov Models for Behavior Recognition ==

The ability of Hierarchical Markov Models to capture hierarchical decomposition makes them an appealing vehicle for capturing the hierarchical nature of human activities. It also allows for model re-usability .<ref name="HierarchicalLearningAndPlanningInPOMDPs"> G. Theocharous [http://dl.acm.org/citation.cfm?id=936140 Hierarchical Learning and Planning in Partially Observable Markov Decision Processes]. PhD thesis, 2002. </ref> and certain conditional independence properties between different levels of abstraction in the model allow for faster learning and inference <ref name="AHMM" /> <ref name="RecognitionOfHumanActivityThroughHierarchicalStochasticLearning" />.

== References ==

{{Reflist}}

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[[:Category:Machine_learning]]

[[:Category:Artificial_intelligence]]

{{AfC postpone G13|1}}