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MIPOSE: a micro-intelligent platform for dynamic human pose recognition

Published: 22 May 2020 Publication History

Abstract

Giving computers the ability to learn from demonstrations is important for users to perform complex tasks. In this paper, we present an intelligent self-learning interface for dynamic human pose recognition. We capture 20 samples for an unknown pose to train a stable generative adversarial networks (GAN) system which aims to conduct data enhancement, then we adopt a threshold isolation method to distinguish relatively similar poses. A few minutes of learning time is sufficient to train a GAN system to successfully generate qualified pose samples. Our platform provides a feasible scheme for micro-intelligent interface, which can benefit to human-robot interaction greatly.

References

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Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, and Gang Hua. 2017. CVAE-GAN: Fine-Grained Image Generation Through Asymmetric Training. In The IEEE International Conference on Computer Vision (ICCV).
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Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2017. Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3]
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2018. StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4]
Nishanth Koganti, Abdul Rahman H. A. G., Yusuke Iwasawa, Kotaro Nakayama, and Yutaka Matsuo. 2018. Virtual Reality As a User-friendly Interface for Learning from Demonstrations. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA '18). ACM, New York, NY, USA, Article D310, 4 pages.
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Behnam Neyshabur, Srinadh Bhojanapalli, David Mcallester, and Nati Srebro. 2017. Exploring Generalization in Deep Learning. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 5947--5956.
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Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015).

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cover image ACM Conferences
AsianHCI '19: Proceedings of Asian CHI Symposium 2019: Emerging HCI Research Collection
May 2019
190 pages
ISBN:9781450366793
DOI:10.1145/3309700
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 May 2020

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Author Tags

  1. DCGAN
  2. human-robot interaction
  3. micro-intelligent interface
  4. pose recognition

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  • Extended-abstract

Funding Sources

  • National Natural Science Foundation of China
  • National Key Research and Development Program of China

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CHI '19
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