Cilla, RodrigoPatricio Guisado, Miguel ÁngelBerlanga de Jesús, AntonioMolina López, José Manuel2015-07-142015-07-142014-09Expert Systems (2014). 31(4), 354-3641468-0394 (online)0266-4720 (print)https://hdl.handle.net/10016/21416Employing 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.11application/pdfeng© 2013 Wiley Publishing Ltd.Human Action RecognitionMultiple View LearningL1 regularizationresearch articleInformáticahttps://doi.org/10.1111/exsy.12040open access3544364Expert systems31AR/0000015753