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This paper presents an image classification based approach for skeleton-basedvideo action recognition problem. Firstly, A dataset independenttranslation-scale invariant image mapping method is proposed, which transformesthe skeleton videos to colour images, named skeleton-images. Secondly, Amulti-scale deep convolutional neural network (CNN) architecture is proposedwhich could be built and fine-tuned on the powerful pre-trained CNNs, e.g.,AlexNet, VGGNet, ResNet etal.. Even though the skeleton-images are verydifferent from natural images, the fine-tune strategy still works well. Atlast, we prove that our method could also work well on 2D skeleton video data.We achieve the state-of-the-art results on the popular benchmard datasets e.g.NTU RGB+D, UTD-MHAD, MSRC-12, and G3D. Especially on the largest and challengeNTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methodsby a large margion, which proves the efficacy of the proposed method.

Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn