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Asian Conference on Computer Vision

ACCV 2018: Computer Vision – ACCV 2018 pp 431–446Cite as

Cross-View Action Recognition Using View-Invariant Pose Feature Learned from Synthetic Data with Domain Adaptation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11362))

Abstract

Recognizing human activities from unknown views is a challenging problem since human shapes appear quite differently from different viewpoints. In this paper, we learn a View-Invariant Pose (VIP) feature for depth-based cross-view action recognition. The proposed VIP feature encoder is a deep convolutional neural network that transfers human poses from multiple viewpoints to a shared high-level feature space. Learning such a deep model requires a large corpus of multi-view paired data which is very expensive to collect. Therefore, we generate a synthetic dataset by fitting human physical models with real motion capture data in the simulators and rendering depth images from various viewpoints. The VIP feature is learned from the synthetic data in an unsupervised way. To ensure the transferability from synthetic data to real data, domain adaptation is employed to minimize the domain difference. Moreover, an action can be considered as a sequence of poses and the temporal progress is modeled by recurrent neural network. In the experiments, our method is applied on two benchmark datasets of multi-view 3D human action and has been shown to achieve promising results when compared with the state-of-the-arts.

Supported by NTU Research Center for AI and Advanced Robotics and the Ministry of Science and Technology (MOST) under the Grants No. MOST 107-2634-F-002-018- and MOST 106-2218-E-002-043.

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Author information

Authors and Affiliations

  1. National Taiwan University, Taipei, 10617, Taiwan

    Yu-Huan Yang, An-Sheng Liu, Yu-Hung Liu, Tso-Hsin Yeh, Zi-Jun Li & Li-Chen Fu

  2. NTU Research Center for AI and Advanced Robotics, Taipei, 10617, Taiwan

    Li-Chen Fu

Authors
  1. Yu-Huan Yang
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  2. An-Sheng Liu
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  3. Yu-Hung Liu
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  4. Tso-Hsin Yeh
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  5. Zi-Jun Li
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  6. Li-Chen Fu
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Corresponding author

Correspondence to Li-Chen Fu .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C. V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Yang, YH., Liu, AS., Liu, YH., Yeh, TH., Li, ZJ., Fu, LC. (2019). Cross-View Action Recognition Using View-Invariant Pose Feature Learned from Synthetic Data with Domain Adaptation. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11362. Springer, Cham. https://doi.org/10.1007/978-3-030-20890-5_28

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  • DOI: https://doi.org/10.1007/978-3-030-20890-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20889-9

  • Online ISBN: 978-3-030-20890-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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