• DocumentCode
    253908
  • Title

    DeepPose: Human Pose Estimation via Deep Neural Networks

  • Author

    Toshev, Alexander ; Szegedy, Christian

  • Author_Institution
    Google, Mountain View, CA, USA
  • fYear
    2014
  • fDate
    23-28 June 2014
  • Firstpage
    1653
  • Lastpage
    1660
  • Abstract
    We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regres- sors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formula- tion which capitalizes on recent advances in Deep Learn- ing. We present a detailed empirical analysis with state-of- art or better performance on four academic benchmarks of diverse real-world images.
  • Keywords
    neural nets; pose estimation; regression analysis; DNN-based regression problem; DeepPose; academic benchmarks; body joints; deep neural networks; human pose estimation; real-world images; Computational modeling; Detectors; Estimation; Joints; Measurement; Training; Vectors; cascades; deep learning; human pose estimation; neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
  • Conference_Location
    Columbus, OH
  • Type

    conf

  • DOI
    10.1109/CVPR.2014.214
  • Filename
    6909610