3D POSE ESTIMATION BY GROUPED FEATURE FUSION AND MOTION AMPLITUDE ENCODING

Jihua Peng, Yanghong Zhou, P. Y. Mok

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

3D human pose estimation is challenging and converting it to a local pose estimation problem by dividing human body into different groups based on anatomical relationships can improve the accuracy of the resulting 3D pose estimation. Joint features of different groups are fused to predict complete pose information of entire body, in which a joint feature fusion scheme must be used for this purpose. However, the joint feature fusion adopted in existing methods has to learn a large number of parameters and is computational expensive. In this paper, we propose an optimized feature fusion (OFF) module, which requires fewer parameters and less calculations while ensures prediction accuracy. Moreover, we also propose a motion amplitude encoding (MAE) method to improve the prediction accuracy for small ranged movements. Experiments have shown that our method outperforms previous state-of-the-art results on Human3.6M dataset.

Original languageEnglish
Title of host publication16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
PublisherIADIS Press
Pages27-34
Number of pages8
ISBN (Electronic)9789898704429
Publication statusPublished - Jul 2022
Event16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022 - Lisbon, Portugal
Duration: 19 Jul 202222 Jul 2022

Publication series

Name16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022

Conference

Conference16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
Country/TerritoryPortugal
CityLisbon
Period19/07/2222/07/22

Keywords

  • Feature Fusion
  • Human Pose Estimation
  • Motion Amplitude

ASJC Scopus subject areas

  • General Computer Science

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