HHMM Based Recognition of Human Activity

Daiki KAWANAKA; Takayuki OKATANI; Koichiro DEGUCHI

doi:10.1093/ietisy/e89-d.7.2180

HHMM Based Recognition of Human Activity

Daiki KAWANAKA, Takayuki OKATANI, Koichiro DEGUCHI

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In this paper, we present a method for recognition of human activity as a series of actions from an image sequence. The difficulty with the problem is that there is a chicken-egg dilemma that each action needs to be extracted in advance for its recognition but the precise extraction is only possible after the action is correctly identified. In order to solve this dilemma, we use as many models as actions of our interest, and test each model against a given sequence to find a matched model for each action occurring in the sequence. For each action, a model is designed so as to represent any activity containing the action. The hierarchical hidden Markov model (HHMM) is employed to represent the models, in which each model is composed of a submodel of the target action and submodels which can represent any action, and they are connected appropriately. Several experimental results are shown.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2180-2185

Publication Date: 2006/07/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.7.2180

Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)

Category: Face, Gesture, and Action Recognition

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Daiki KAWANAKA, Takayuki OKATANI, Koichiro DEGUCHI, "HHMM Based Recognition of Human Activity" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 7, pp. 2180-2185, July 2006, doi: 10.1093/ietisy/e89-d.7.2180.
Abstract: In this paper, we present a method for recognition of human activity as a series of actions from an image sequence. The difficulty with the problem is that there is a chicken-egg dilemma that each action needs to be extracted in advance for its recognition but the precise extraction is only possible after the action is correctly identified. In order to solve this dilemma, we use as many models as actions of our interest, and test each model against a given sequence to find a matched model for each action occurring in the sequence. For each action, a model is designed so as to represent any activity containing the action. The hierarchical hidden Markov model (HHMM) is employed to represent the models, in which each model is composed of a submodel of the target action and submodels which can represent any action, and they are connected appropriately. Several experimental results are shown.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2180/_p

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@ARTICLE{e89-d_7_2180,
author={Daiki KAWANAKA, Takayuki OKATANI, Koichiro DEGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={HHMM Based Recognition of Human Activity},
year={2006},
volume={E89-D},
number={7},
pages={2180-2185},
abstract={In this paper, we present a method for recognition of human activity as a series of actions from an image sequence. The difficulty with the problem is that there is a chicken-egg dilemma that each action needs to be extracted in advance for its recognition but the precise extraction is only possible after the action is correctly identified. In order to solve this dilemma, we use as many models as actions of our interest, and test each model against a given sequence to find a matched model for each action occurring in the sequence. For each action, a model is designed so as to represent any activity containing the action. The hierarchical hidden Markov model (HHMM) is employed to represent the models, in which each model is composed of a submodel of the target action and submodels which can represent any action, and they are connected appropriately. Several experimental results are shown.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2180},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - HHMM Based Recognition of Human Activity
T2 - IEICE TRANSACTIONS on Information
SP - 2180
EP - 2185
AU - Daiki KAWANAKA
AU - Takayuki OKATANI
AU - Koichiro DEGUCHI
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2180
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - In this paper, we present a method for recognition of human activity as a series of actions from an image sequence. The difficulty with the problem is that there is a chicken-egg dilemma that each action needs to be extracted in advance for its recognition but the precise extraction is only possible after the action is correctly identified. In order to solve this dilemma, we use as many models as actions of our interest, and test each model against a given sequence to find a matched model for each action occurring in the sequence. For each action, a model is designed so as to represent any activity containing the action. The hierarchical hidden Markov model (HHMM) is employed to represent the models, in which each model is composed of a submodel of the target action and submodels which can represent any action, and they are connected appropriately. Several experimental results are shown.
ER -