Output details
11 - Computer Science and Informatics
University of Dundee
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Article title
Action categorization with modified hidden conditional random field
Type
D - Journal article
Title of journal
Pattern Recognition
Article number
-
Volume number
43
Issue number
1
First page of article
197
ISSN of journal
0031-3203
Year of publication
2010
URL
-
Number of additional authors
1
Additional information
<23> This paper advanced the hidden conditional random field (HCRF) for action recognition. Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. A modified HCRF (mHCRF) is then formulated to have a global optimum in the modelling of the temporal action dependencies after the pathing stage. Rigorous experimental results on action categorization using this model are compared favourably against several existing model- based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF. This work is supported by INSIGHT (GR/S63687/01) project funded by EPSRC and MOD DSTL
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
34
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
