Enhanced Two-Stage Multi-person Pose Estimation

Hiroto Honda, Tomohiro Kato, Yusuke Uchida

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.