Flexible top-view human pose estimation for detection system via CNN

Ryuji Go, Yoshimitsu Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We propose the DeepPose-based pose estimation system that is flexible with the change of bounding-box range for top-view images. Our purpose is to link person detection system and pose estimation system. We introduce Bounding-box Curriculum Learning (BCL) and Recurrent Pose Estimation (RPE). BCL is a learning technique of CNN inspired from Curriculum Learning. RPE is a recurrent process of pose estimation that fixes the bounding-box range in response to the estimated results. We show the effect of proposed methods compared to normal learned CNN-based pose estimator on our original top-view dataset.

Original languageEnglish
Title of host publication2016 IEEE 5th Global Conference on Consumer Electronics, GCCE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509023332
DOIs
Publication statusPublished - 2016 Dec 27
Event5th IEEE Global Conference on Consumer Electronics, GCCE 2016 - Kyoto, Japan
Duration: 2016 Oct 112016 Oct 14

Other

Other5th IEEE Global Conference on Consumer Electronics, GCCE 2016
Country/TerritoryJapan
CityKyoto
Period16/10/1116/10/14

Keywords

  • Convolutional Neural Networks
  • Pose Estimation
  • Top-view

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Hardware and Architecture
  • Instrumentation

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