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MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

  • Arjun JainEmail author
  • Jonathan Tompson
  • Yann LeCun
  • Christoph Bregler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion (This dataset can be downloaded from http://cs.nyu.edu/~ajain/accv2014/.), that extends the FLIC dataset [1] with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.

Keywords

Optical Flow Motion Feature Laplacian Pyramid Convolutional Layer Convolutional Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank Tyler Zhu for his help with the data-set creation. This research was funded in part by the Office of Naval Research ONR Award N000141210327.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arjun Jain
    • 1
    Email author
  • Jonathan Tompson
    • 1
  • Yann LeCun
    • 1
  • Christoph Bregler
    • 1
  1. 1.New York UniversityNew YorkUSA

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