Joint Distance Maps Based Action Recognition With Convolutional Neural Networks

Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit...

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Bibliographic Details
Published in:IEEE signal processing letters 2017-05, Vol.24 (5), p.624-628
Main Authors: Li, Chuankun, Hou, Yonghong, Wang, Pichao, Li, Wanqing
Format: Article
Language:English
Subjects:
Action recognition
convolutional neural networks (ConvNets)
Image color analysis
Image recognition
joint distance maps (JDM)
Legged locomotion
Neural networks
Skeleton
Three-dimensional displays
Training
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Summary:Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2017.2678539