Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks
This letter presents an effective method to encode the spatiotemporal information of a skeleton sequence into color texture images, referred to as skeleton optical spectra, and employs convolutional neural networks (ConvNets) to learn the discriminative features for action recognition. Such spectrum...
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| Published in | IEEE transactions on circuits and systems for video technology Vol. 28; no. 3; pp. 807 - 811 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.03.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This letter presents an effective method to encode the spatiotemporal information of a skeleton sequence into color texture images, referred to as skeleton optical spectra, and employs convolutional neural networks (ConvNets) to learn the discriminative features for action recognition. Such spectrum representation makes it possible to use a standard ConvNet architecture to learn suitable "dynamic" features from skeleton sequences without training millions of parameters afresh and it is especially valuable when there is insufficient annotated training video data. Specifically, the encoding consists of four steps: mapping of joint distribution, spectrum coding of joint trajectories, spectrum coding of body parts, and joint velocity weighted saturation and brightness. Experimental results on three widely used datasets have demonstrated the efficacy of the proposed method. |
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| AbstractList | This letter presents an effective method to encode the spatiotemporal information of a skeleton sequence into color texture images, referred to as skeleton optical spectra, and employs convolutional neural networks (ConvNets) to learn the discriminative features for action recognition. Such spectrum representation makes it possible to use a standard ConvNet architecture to learn suitable “dynamic” features from skeleton sequences without training millions of parameters afresh and it is especially valuable when there is insufficient annotated training video data. Specifically, the encoding consists of four steps: mapping of joint distribution, spectrum coding of joint trajectories, spectrum coding of body parts, and joint velocity weighted saturation and brightness. Experimental results on three widely used datasets have demonstrated the efficacy of the proposed method. |
| Author | Hou, Yonghong Wang, Pichao Li, Wanqing Li, Zhaoyang |
| Author_xml | – sequence: 1 givenname: Yonghong surname: Hou fullname: Hou, Yonghong email: houroy@tju.edu.cn organization: School of Electronic Information Engineering, Tianjin University, Tianjin, China – sequence: 2 givenname: Zhaoyang surname: Li fullname: Li, Zhaoyang email: lizhaoyang@tju.edu.cn organization: School of Electronic Information Engineering, Tianjin University, Tianjin, China – sequence: 3 givenname: Pichao orcidid: 0000-0002-1430-0237 surname: Wang fullname: Wang, Pichao email: pw212@uowmail.edu.au organization: Advanced Multimedia Research Lab, University of Wollongong, Wollongong, NSW, Australia – sequence: 4 givenname: Wanqing orcidid: 0000-0002-4427-2687 surname: Li fullname: Li, Wanqing email: wanqing@uow.edu.au organization: Advanced Multimedia Research Lab, University of Wollongong, Wollongong, NSW, Australia |
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| SubjectTerms | Action recognition Artificial neural networks Body parts Coding Color texture convolutional neural network (ConvNet) Encoding Feature recognition Image coding Image color analysis Image recognition Joints (anatomy) Mapping Neural networks Skeleton Training Video data |
| Title | Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks |
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