Presentation 2017-10-13
Robust Human Pose Estimation from Distorted Images
Daisuke Miki, Shinya Abe,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Predicting a human pose from an image is a technique used for detection of abnormalities. However, existing motion capture systems need complicated imaging devices such as stereo camera or infrared camera. It is also difficult to recognize an individual’s pose from a short distance using the devices in use today. In this study, we present a robust human pose estimation method using a distorted fisheye image with a device that can capture images from a wide-angle as well as at a close-range. We propose a multi-layered convolutional neural network architecture for estimating an individual’s joint positions and deformation parameters to enable robustness to image distortion. We confirmed the ability of wide-angle and close-range detection through the publicly available datasets.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Human pose estimation / Deep learning / Convolutional neural network / Fisheye image
Paper # PRMU2017-93
Date of Issue 2017-10-05 (PRMU)

Conference Information
Committee PRMU
Conference Date 2017/10/12(2days)
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Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Shinichi Sato(NII)
Vice Chair Hironobu Fujiyoshi(Chubu Univ.) / Yoshihisa Ijiri(Omron)
Secretary Hironobu Fujiyoshi(AIST) / Yoshihisa Ijiri(NAIST)
Assistant Masato Ishii(NEC) / Yusuke Sugano(Osaka Univ.)

Paper Information
Registration To Technical Committee on Pattern Recognition and Media Understanding
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Robust Human Pose Estimation from Distorted Images
Sub Title (in English)
Keyword(1) Human pose estimation
Keyword(2) Deep learning
Keyword(3) Convolutional neural network
Keyword(4) Fisheye image
1st Author's Name Daisuke Miki
1st Author's Affiliation Tokyo Metropolitan Industrial Technology Research Institute(TIRI)
2nd Author's Name Shinya Abe
2nd Author's Affiliation Tokyo Metropolitan Industrial Technology Research Institute(TIRI)
Date 2017-10-13
Paper # PRMU2017-93
Volume (vol) vol.117
Number (no) PRMU-238
Page pp.pp.169-174(PRMU),
#Pages 6
Date of Issue 2017-10-05 (PRMU)