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Enhanced Two-Stage Multi-person Pose Estimation

  • Hiroto HondaEmail author
  • Tomohiro Kato
  • Yusuke Uchida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)

Abstract

In this paper we introduce an enhanced multi-person pose estimation method for the competition of the PoseTrack [6] workshop in ECCV 2018. We employ a two-stage human pose detector, where human region detection and keypoint detection are separately performed. A strong encoder-decoder network for keypoint detection has achieved 70.4% mAP for PoseTrack 2018 validation dataset.

Keywords

Multi-person pose estimation Keypoint detection 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DeNA Co., Ltd.TokyoJapan

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