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

Ryuji Go, Yoshimitsu Aoki

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2016 IEEE 5th Global Conference on Consumer Electronics, GCCE 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509023332
DOI
出版ステータスPublished - 2016 12月 27
イベント5th IEEE Global Conference on Consumer Electronics, GCCE 2016 - Kyoto, Japan
継続期間: 2016 10月 112016 10月 14

Other

Other5th IEEE Global Conference on Consumer Electronics, GCCE 2016
国/地域Japan
CityKyoto
Period16/10/1116/10/14

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • 器械工学

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