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Human Pose Estimation by a Series of Residual Auto-Encoders

  • M. FarrajotaEmail author
  • João M. F. Rodrigues
  • J. M. H. du Buf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10255)

Abstract

Pose estimation is the task of predicting the pose of an object in an image or in a sequence of images. Here, we focus on articulated human pose estimation in scenes with a single person. We employ a series of residual auto-encoders to produce multiple predictions which are then combined to provide a heatmap prediction of body joints. In this network topology, features are processed across all scales which captures the various spatial relationships associated with the body. Repeated bottom-up and top-down processing with intermediate supervision for each auto-encoder network is applied. We propose some improvements to this type of regression-based networks to further increase performance, namely: (a) increase the number of parameters of the auto-encoder networks in the pipeline, (b) use stronger regularization along with heavy data augmentation, (c) use sub-pixel precision for more precise joint localization, and (d) combine all auto-encoders output heatmaps into a single prediction, which further increases body joint prediction accuracy. We demonstrate state-of-the-art results on the popular FLIC and LSP datasets.

Keywords

Human pose ConvNet Neural networks Auto-encoders 

Notes

Acknowledgments

This work was supported by the FCT project LARSyS (UID/EEA/50009/2013) and FCT PhD grant to author MF (SFRH/BD/79812/2011).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Vision Laboratory, LARSySUniversity of the AlgarveFaroPortugal

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