End-to-end feature pyramid network for real-time multi-person pose estimation

Dingli Luo, Songlin Du, Takeshi Ikenaga

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

1 Citation (Scopus)

Abstract

In computer vision, pose estimation system is widely used to construct human body transformation. However, it is hard to achieve these targets together: Stable real-time speed, variance human number and high accuracy. This paper proposes an end-to-end pose estimation network. It contains a neural network friendly representation of human pose. Then it proposes a correspond real-time end-to-end pose estimation network based on feature pyramid network structure with attention-based detection modules. This network can detect multiple humans in more than 60 fps with 384 x 384 resolution on GTX 1070 with affordable accuracy. This work shows the potential of this network structure can perform both faster and better compared with state-of-the-art results.

Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784901122184
DOIs
Publication statusPublished - 2019 May
Externally publishedYes
Event16th International Conference on Machine Vision Applications, MVA 2019 - Tokyo, Japan
Duration: 2019 May 272019 May 31

Publication series

NameProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019

Conference

Conference16th International Conference on Machine Vision Applications, MVA 2019
CountryJapan
CityTokyo
Period19/5/2719/5/31

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Computer Vision and Pattern Recognition

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  • Cite this

    Luo, D., Du, S., & Ikenaga, T. (2019). End-to-end feature pyramid network for real-time multi-person pose estimation. In Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019 [8758029] (Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/MVA.2019.8758029