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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.
== Examples of Hierarchical Markov Models ==
=== Hierarchical Hidden Markov Model ===
A [[Hierarchical hidden Markov model|Hierarchical Hidden Markov Model]] (HHMM) is an extension of a [[Hidden Markov model|Hidden Markov Model]] that contains states at different levels of abstraction. Thus, the structure of an HHMM is more suitable for representing the hierarchy in human activities. For instance, the activity of eating dinner could be decomposed in the activities of preparing food, cooking, cleaning, and washing dishes. A corresponding HHMM would have a high-level state corresponding to the activity of eating dinner and low-level states corresponding to the sub-activities. The reader is referred to the article on [[Hierarchical hidden Markov model|Hierarchical Hidden Markov Models]] for more detailed information.
=== Abstract Hidden Markov Model ===
An Abstract Hidden Markov Model <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, December 2002.</ref>
== References ==
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