  We propose a novel skeleton-based representation for 3D action recognition in
videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have
been addressed: First, how to construct a robust representation that easily
captures the spatial-temporal evolutions of motions from skeleton sequences.
Second, how to design D-CNNs capable of learning discriminative features from
the new representation in a effective manner. To address these tasks, a
skeletonbased representation, namely, SPMF (Skeleton Pose-Motion Feature) is
proposed. The SPMFs are built from two of the most important properties of a
human action: postures and their motions. Therefore, they are able to
effectively represent complex actions. For learning and recognition tasks, we
design and optimize new D-CNNs based on the idea of Inception Residual networks
to predict actions from SPMFs. Our method is evaluated on two challenging
datasets including MSR Action3D and NTU-RGB+D. Experimental results indicated
that the proposed method surpasses state-of-the-art methods whilst requiring
less computation.
