Abstract
A methodology for automatic segmentation and classification of multi-channel data related to motion capture (MoCap) videos of cyclic activities are presented. Regarding this, a kernel approach is employed to obtain a time representation, which captures the cyclic behavior of a given multi-channel data. Moreover, we calculate a mapping based on kernel principal component analysis, in order to obtain a low-dimensional space that encodes the main cyclic behaviors. From such, low-dimensional space the main segments of the studied activity are inferred. Then, a distance based classifier is used to classified each MoCap video segment. A well-known MoCap database is tested which contains different activities performed by humans. Attained results shows how our approach is a simple alternative to obtain a suitable classification performance in comparison to complex methods for MoCap analysis.
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García-Vega, S., Álvarez-Meza, A.M., Castellanos-Domínguez, C.G. (2013). MoCap Data Segmentation and Classification Using Kernel Based Multi-channel Analysis. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41827-3_62
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DOI: https://doi.org/10.1007/978-3-642-41827-3_62
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