3D Human Motion Prediction Based on Bi-directionalGated Recurrent Unit

Haifeng SANG; Zizhen CHEN

doi:10.11999/JEIT180978

Volume 41 Issue 9

Sep. 2019

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2256-2263

Haifeng SANG, Zizhen CHEN. 3D Human Motion Prediction Based on Bi-directionalGated Recurrent Unit[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2256-2263. doi: 10.11999/JEIT180978

Citation:

Haifeng SANG, Zizhen CHEN. 3D Human Motion Prediction Based on Bi-directionalGated Recurrent Unit[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2256-2263. doi: 10.11999/JEIT180978

Haifeng SANG, Zizhen CHEN. 3D Human Motion Prediction Based on Bi-directionalGated Recurrent Unit[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2256-2263. doi: 10.11999/JEIT180978

Citation:

PDF( 2317 KB)

3D Human Motion Prediction Based on Bi-directionalGated Recurrent Unit

doi: 10.11999/JEIT180978

Haifeng SANG,
Zizhen CHEN^,

School of Information Science & Engineering, Shenyang University of Technology, Shenyang 110870, China

Funds: The National Natural Science Foundation of China (61773105), The Natural Science Foundation of Liaoning Province (20170540675), The Research Project of Liaoning Provincial Department of Education (LQGD2017023)

Received Date: 2018-10-19
Rev Recd Date: 2019-03-08

Available Online: 2019-04-09

Publish Date: 2019-09-10

Abstract

Abstract

In the field of computer vision, predicting human motion is very necessary for timely human–computer interaction and personnel tracking. In order to improve the performance of human–computer interaction and personnel tracking, an encoder-decoder model called Bi–directional Gated Recurrent Unit Encoder–Decoder (EBiGRU–D) based on Gated Recurrent Unit (GRU) is proposed to learn 3D human motion and give a prediction of motion over a period of time. EBiGRU–D is a deep Recurrent Neural Network (RNN) in which the encoder is a Bidirectional GRU (BiGRU) unit and the decoder is a unidirectional GRU unit. BiGRU allows raw data to be simultaneously input from both the forward and reverse directions and then encoded into a state vector, which is then sent to the decoder for decoding. BiGRU associates the current output with the state of the front and rear time, so that the output fully considers the characteristics of the time before and after, so that the prediction is more accurate. Experimental results on the human3.6m dataset demonstrate that EBiGRU–D not only improves greatly the error of 3D human motion prediction but also increases greatly the time for accurate prediction.
- Human motion prediction,
- 3D motion recognition,
- Recurrent Neural Network (RNN),
- Deep learning

FullText(HTML)

References(16)

References

FOKA A F and TRAHANIAS P E. Probabilistic autonomous robot navigation in dynamic environments with human motion prediction[J]. International Journal of Social Robotics, 2010, 2(1): 79–94. doi: 10.1007/s12369-009-0037-z

MAINPRICE J and BERENSON D. Human–robot collaborative manipulation planning using early prediction of human motion[C]. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 2013: 299–306.

BÜTEPAGE J, BLACK M J, KRAGIC D, et al. Deep representation learning for human motion prediction and classification[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 1591–1599.

TEKIN B, MÁRQUEZ–NEILA P, SALZMANN M, et al. Learning to fuse 2D and 3D image cues for monocular body pose estimation[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 3961–3970.

YASIN H, IQBAL U, KRÜGER B, et al. A dual–source approach for 3D pose estimation from a single image[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 4948–4956.

肖俊, 庄越挺, 吴飞. 三维人体运动特征可视化与交互式运动分割[J]. 软件学报, 2008, 19(8): 1995–2003.

XIAO Jun, ZHUANG Yueting, and WU Fei. Feature visualization and interactive segmentation of 3D human motion[J]. Journal of Software, 2008, 19(8): 1995–2003.

潘红, 肖俊, 吴飞, 等. 基于关键帧的三维人体运动检索[J]. 计算机辅助设计与图形学学报, 2009, 21(2): 214–222.

PAN Hong, XIAO Jun, WU Fei, et al. 3D human motion retrieval based on key-frames[J]. Journal of Computer-Aided Design &Computer Graphics, 2009, 21(2): 214–222.

LI Rui, LIU Zhenyu, and TAN Jianrong. Human motion segmentation using collaborative representations of 3D skeletal sequences[J]. IET Computer Vision, 2018, 12(4): 434–442. doi: 10.1049/iet-cvi.2016.0385

TAYLOR G W, HINTON G E, and ROWEIS S. Modeling human motion using binary latent variables[C]. The 19th International Conference on Neural Information Processing Systems, Hong Kong, China, 2006: 1345–1352.

FRAGKIADAKI K, LEVINE S, FELSEN P, et al. Recurrent network models for human dynamics[C]. The IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 4346–4354.

HOLDEN D, SAITO J, and KOMURA T. A deep learning framework for character motion synthesis and editing[J]. ACM Transactions on Graphics, 2016, 35(4): 1–11.

ASHESH J, ZAMIR A R, SAVARESE S, et al. Structural-RNN: Deep learning on spatio–temporal graphs[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 5308–5317.

MARTINEZ J, BLACK M J, and ROMERO J. On human motion prediction using recurrent neural networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 4674–4683.

TANG Yongyi, MA Lin, LIU Wei, et al. Long–term human motion prediction by modeling motion context and enhancing motion dynamic[J/OL]. arXiv: 1805.02513. http://arxiv.org/abs/1805.02513, 2018.

ZHANG Yachao, LIU Kaipei, QIN Liang, et al. Deterministic and probabilistic interval prediction for short&-term wind power generation based on variational mode decomposition and machine learning methods[J]. Energy Conversion and Management, 2016, 112: 208–219. doi: 10.1016/j.enconman.2016.01.023

CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J/OL]. arXiv: 1406.1078, 2014.

Relative Articles

Supplements(0)

Cited By

Proportional views