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Einat Marhasev, Meirav Hadad, and Gal A. Kaminka. Non-stationary Hidden
Semi Markov Models in Activity Recognition. In Proceedings of the AAAI Workshop on Modeling Others from Observations
(MOO-06), 2006.
Activity recognition is a process by which the ongoing observed behavior of an agent is tracked and mapped to a given model, explaining the behavior and accounting for hidden or unobservable state (e.g., goals or beliefs of the obser ved agents). Various methods for activity recognition exist. A popular family of such methods rely on Hidden Markov Models HMMs and variants for recognition. These models, however, do not account for changes in transition probabilities base d on the duration an agent has spent in a given state. This paper investigates Markov models that go beyond existing models, to explicitly model the dependency of transition probabilities on state duration. In particular, we propose the use of Non-stationary Hidden Semi Markov Models (NHSMMs) in activity recognition. We present the NHSMM model, and compare its performance in recognizing normal and abnormal behavior, using synthetic da ta from an industry simulator. We show that for relatively simple activity recognition tasks, both HSMMs and NHSMMs easily and significantly outperform HMMs. In more complex tasks, the NHSMMs also outperf orm the HSMMs, and allow significantly more accurately recognition.
@InProceedings{moo06einat,
author = {Einat Marhasev and Meirav Hadad and Gal A. Kaminka},
title = {Non-stationary Hidden Semi Markov Models in Activity Recognition},
OPTcrossref = {},
OPTkey = {},
booktitle = MOO-06,
OPTpages = {},
year = {2006},
abstract = {
Activity recognition is a process by which the ongoing observed
behavior of an agent is tracked and mapped to a given model,
explaining the behavior and accounting for hidden or
unobservable state (e.g., goals or beliefs of the obser ved
agents). Various methods for activity recognition exist. A
popular family of such methods rely on Hidden Markov Models HMMs
and variants for recognition. These models, however, do not
account for changes in transition probabilities base d on
the duration an agent has spent in a given state. This paper
investigates Markov models that go beyond existing models, to
explicitly model the dependency of transition probabilities on
state duration. In particular, we propose the use of
Non-stationary Hidden Semi Markov Models (NHSMMs) in activity
recognition. We present the NHSMM model, and compare its
performance in recognizing normal and abnormal behavior, using
synthetic da ta from an industry simulator. We show that for
relatively simple activity recognition tasks, both HSMMs and
NHSMMs easily and significantly outperform HMMs. In more complex
tasks, the NHSMMs also outperf orm the HSMMs, and allow
significantly more accurately recognition.
},
wwwnote = {},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
OPTpublisher = {},
OPTnote = {},
OPTannote = {}
}
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