Title
Multicamera action recognition with canonical correlation analysis and discriminative sequence classification
Abstract
This paper presents a feature fusion approach to the recognition of human actions from multiple cameras that avoids the computation of the 3D visual hull. Action descriptors are extracted for each one of the camera views available and projected into a common subspace that maximizes the correlation between each one of the components of the projections. That common subspace is learned using Probabilistic Canonical Correlation Analysis. The action classification is made in that subspace using a discriminative classifier. Results of the proposed method are shown for the classification of the IXMAS dataset.
Year
DOI
Venue
2011
10.1007/978-3-642-21344-1_51
IWINAC (1)
Keywords
Field
DocType
multiple camera,discriminative sequence classification,discriminative classifier,multicamera action recognition,common subspace,ixmas dataset,action descriptors,probabilistic canonical correlation analysis,human action,canonical correlation analysis,feature fusion approach,action classification
Visual hull,Computer science,Canonical correlation,Artificial intelligence,Probabilistic logic,Classifier (linguistics),Discriminative model,Computation,Computer vision,Pattern recognition,Subspace topology,Correlation,Machine learning
Conference
Volume
ISSN
Citations 
6686
0302-9743
1
PageRank 
References 
Authors
0.35
18
4
Name
Order
Citations
PageRank
Rodrigo Cilla1385.26
Miguel A. Patricio230538.05
Antonio Berlanga319623.09
José M. Molina460467.82