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Human action recognition with sparse classification and multiple‐view learning
Author(s) -
Cilla Rodrigo,
Patricio Miguel A.,
Berlanga Antonio,
Molina José M.
Publication year - 2014
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12040
Subject(s) - computer science , viewpoints , artificial intelligence , classifier (uml) , pattern recognition (psychology) , action recognition , regularization (linguistics) , dimensionality reduction , curse of dimensionality , feature vector , machine learning , art , visual arts , class (philosophy)
Abstract Employing multiple camera viewpoints in the recognition of human actions increases performance. This paper presents a feature fusion approach to efficiently combine 2D observations extracted from different camera viewpoints. Multiple‐view dimensionality reduction is employed to learn a common parameterization of 2D action descriptors computed for each one of the available viewpoints. Canonical correlation analysis and their variants are employed to obtain such parameterizations. A sparse sequence classifier based on L1 regularization is proposed to avoid the problem of having to choose the proper number of dimensions of the common parameterization. The proposed system is employed in the classification of the Inria Xmas Motion Acquisition Sequences (IXMAS) data set with successful results.