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

Dingli Luo, Songlin Du, Takeshi Ikenaga

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトルProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
出版者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9784901122184
DOI
出版物ステータスPublished - 2019 5
外部発表Yes
イベント16th International Conference on Machine Vision Applications, MVA 2019 - Tokyo, Japan
継続期間: 2019 5 272019 5 31

出版物シリーズ

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

Conference

Conference16th International Conference on Machine Vision Applications, MVA 2019
Japan
Tokyo
期間19/5/2719/5/31

    フィンガープリント

ASJC Scopus subject areas

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

これを引用

Luo, D., Du, S., & Ikenaga, T. (2019). End-to-end feature pyramid network for real-time multi-person pose estimation. : 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