IEICE TRANSACTIONS on Information
View Invariant Human Action Recognition Based on Factorization and HMMs
Summary :
This paper addresses the problem of view invariant action recognition using 2D trajectories of landmark points on human body. It is a challenging task since for a specific action category, the 2D observations of different instances might be extremely different due to varying viewpoint and changes in speed. By assuming that the execution of an action can be approximated by dynamic linear combination of a set of basis shapes, a novel view invariant human action recognition method is proposed based on non-rigid matrix factorization and Hidden Markov Models (HMMs). We show that the low dimensional weight coefficients of basis shapes by measurement matrix non-rigid factorization contain the key information for action recognition regardless of the viewpoint changing. Based on the extracted discriminative features, the HMMs is used for temporal dynamic modeling and robust action classification. The proposed method is tested using real life sequences and promising performance is achieved.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.7 pp.1848-1854
- Publication Date
- 2008/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.7.1848
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision and its Applications)
- Category
Authors
Keyword
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Xi LI, Kazuhiro FUKUI, "View Invariant Human Action Recognition Based on Factorization and HMMs" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 1848-1854, July 2008, doi: 10.1093/ietisy/e91-d.7.1848.
Abstract: This paper addresses the problem of view invariant action recognition using 2D trajectories of landmark points on human body. It is a challenging task since for a specific action category, the 2D observations of different instances might be extremely different due to varying viewpoint and changes in speed. By assuming that the execution of an action can be approximated by dynamic linear combination of a set of basis shapes, a novel view invariant human action recognition method is proposed based on non-rigid matrix factorization and Hidden Markov Models (HMMs). We show that the low dimensional weight coefficients of basis shapes by measurement matrix non-rigid factorization contain the key information for action recognition regardless of the viewpoint changing. Based on the extracted discriminative features, the HMMs is used for temporal dynamic modeling and robust action classification. The proposed method is tested using real life sequences and promising performance is achieved.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.1848/_p
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@ARTICLE{e91-d_7_1848,
author={Xi LI, Kazuhiro FUKUI, },
journal={IEICE TRANSACTIONS on Information},
title={View Invariant Human Action Recognition Based on Factorization and HMMs},
year={2008},
volume={E91-D},
number={7},
pages={1848-1854},
abstract={This paper addresses the problem of view invariant action recognition using 2D trajectories of landmark points on human body. It is a challenging task since for a specific action category, the 2D observations of different instances might be extremely different due to varying viewpoint and changes in speed. By assuming that the execution of an action can be approximated by dynamic linear combination of a set of basis shapes, a novel view invariant human action recognition method is proposed based on non-rigid matrix factorization and Hidden Markov Models (HMMs). We show that the low dimensional weight coefficients of basis shapes by measurement matrix non-rigid factorization contain the key information for action recognition regardless of the viewpoint changing. Based on the extracted discriminative features, the HMMs is used for temporal dynamic modeling and robust action classification. The proposed method is tested using real life sequences and promising performance is achieved.},
keywords={},
doi={10.1093/ietisy/e91-d.7.1848},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - View Invariant Human Action Recognition Based on Factorization and HMMs
T2 - IEICE TRANSACTIONS on Information
SP - 1848
EP - 1854
AU - Xi LI
AU - Kazuhiro FUKUI
PY - 2008
DO - 10.1093/ietisy/e91-d.7.1848
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - This paper addresses the problem of view invariant action recognition using 2D trajectories of landmark points on human body. It is a challenging task since for a specific action category, the 2D observations of different instances might be extremely different due to varying viewpoint and changes in speed. By assuming that the execution of an action can be approximated by dynamic linear combination of a set of basis shapes, a novel view invariant human action recognition method is proposed based on non-rigid matrix factorization and Hidden Markov Models (HMMs). We show that the low dimensional weight coefficients of basis shapes by measurement matrix non-rigid factorization contain the key information for action recognition regardless of the viewpoint changing. Based on the extracted discriminative features, the HMMs is used for temporal dynamic modeling and robust action classification. The proposed method is tested using real life sequences and promising performance is achieved.
ER -
English
