TY - GEN
T1 - Human actions recognition using fuzzy PCA and discriminative hidden model
AU - Ji, Xiaofei
AU - Liu, Honghai
AU - Li, Yibo
PY - 2010
Y1 - 2010
N2 - As a temporal classification problem, visual-based human actions recognition is an important component for some potential applications. In this paper, we combine Fuzzy Principle Component Analysis(Fuzzy PCA) and hidden Conditional Random Fields(HCRFs) to achieve a viewpoint insensitive human action recognition. Fuzzy PCA is used to reduce the dimension of the silhouette image features to obtain the compact representation of action space. HCRFs is applied to model the human actions from different actors and different viewpoints. This method can relax the independence assumption of the generative model. Experiment results on a public dataset demonstrate the effectiveness and robustness of our method.
AB - As a temporal classification problem, visual-based human actions recognition is an important component for some potential applications. In this paper, we combine Fuzzy Principle Component Analysis(Fuzzy PCA) and hidden Conditional Random Fields(HCRFs) to achieve a viewpoint insensitive human action recognition. Fuzzy PCA is used to reduce the dimension of the silhouette image features to obtain the compact representation of action space. HCRFs is applied to model the human actions from different actors and different viewpoints. This method can relax the independence assumption of the generative model. Experiment results on a public dataset demonstrate the effectiveness and robustness of our method.
UR - https://www.scopus.com/pages/publications/78549282063
U2 - 10.1109/FUZZY.2010.5584348
DO - 10.1109/FUZZY.2010.5584348
M3 - 会议稿件
AN - SCOPUS:78549282063
SN - 9781424469208
T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
Y2 - 18 July 2010 through 23 July 2010
ER -