Abstract
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This paper presents a method for extracting discriminative
key poses for skeleton-based action recognition. Poses
are represented by normalized joint locations, velocities and
accelerations of skeleton joints. An extended label consistent
K-SVD (ELC-KSVD) algorithm is proposed for learning the
common and action-specific dictionaries. Discriminative key poses
are represented by the atoms of the action-specific dictionaries.
With the specific dictionaries, sparse codes are obtained for
representing action instances through max pooling and temporal
pyramid. A SVM classifier is trained for action recognition. The
proposed method was evaluated on the MSRC-12 gesture and
MSR-Action 3D datasets. Experimental results have shown that
the proposed method is effective in extracting discriminative key
poses.