Publication: Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks
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2018-09-06
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IEEE
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Abstract
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 skeleton-based 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.
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This paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP)
Keywords
Human action recognition, SPMF, CNNs
Bibliographic citation
Pham, H.H., Khoudour, L., Crouzil, A., Zegers, P. y Velastin, S.A. (2018). Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks. In IEEE International Conference on Image Processing.