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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 taskactivity 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.
