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Title
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Skeleton-based action recognition using spatiotemporal features with convolutional neural networks
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Type
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Presentation
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Keywords
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—Human action recognition, motion-based action recognition, convolutional neural network
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Abstract
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Deep learning methods achieved outstanding performance in 3D action recognition. Due to time-series property of skeleton sequences, skeleton based action recognition methods are mostly based on recurrent neural networks and Long ShortTerm Memory networks. Compared with RRN-based methods, in this paper a new method was proposed to encode two types of spatial features and motion between the 3D coordinates of joints in two frame with eight time-step gap into a color image. Encoding skeleton sequences into an image made it possible to train a convolutional neural network on each type of images. The proposed method achieved state-of-the-art performance on NTU RGB+D action recognition dataset. The results show an accuracy of 84.9% for action classification on this dataset.
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Researchers
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Hassan Khotanlou (Third Researcher), (Second Researcher), (First Researcher)
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