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We propose a novel skeleton-based representation for 3D action recognition invideos using Deep Convolutional Neural Networks (D-CNNs). Two key issues havebeen addressed: First, how to construct a robust representation that easilycaptures the spatial-temporal evolutions of motions from skeleton sequences.Second, how to design D-CNNs capable of learning discriminative features fromthe new representation in a effective manner. To address these tasks, askeletonbased representation, namely, SPMF (Skeleton Pose-Motion Feature) isproposed. The SPMFs are built from two of the most important properties of ahuman action: postures and their motions. Therefore, they are able toeffectively represent complex actions. For learning and recognition tasks, wedesign and optimize new D-CNNs based on the idea of Inception Residual networksto predict actions from SPMFs. Our method is evaluated on two challengingdatasets including MSR Action3D and NTU-RGB+D. Experimental results indicatedthat the proposed method surpasses state-of-the-art methods whilst requiringless computation.

Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks