Learning Topological Representation of 3D Skeleton Dynamics with Persistent Homology for Human Activity Recognition-Reference-Cited by-同舟云学术

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Learning Topological Representation of 3D Skeleton Dynamics with Persistent Homology for Human Activity Recognition

Published:2023-12-05 Issue: Volume:22 Page:2709-2716
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Yang Hao¹,Sun Dong²,Cai Yi-Jun²,Yang Jing¹,Si Xin-Yu¹,Zhou Shu-Min¹,Yan Yan¹

Affiliation:

1. Chinese Academy of Sciences,Shenzhen Institutes of Advanced Technology,Shenzhen,China

2. Xiamen University of Technology,School of Opto-Electronic and Communication Engineering,Xiamen,China

Funder

National Key Research and Development Program of China

Ministry of Science and Technology

Natural Science Foundation of Fujian Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10385684.pdf?arnumber=10385684

Reference38 articles.

1. Action classification from motion capture data using topological data analysis

2. Real-time human pose recognition in parts from single depth images

3. Deep High-Resolution Representation Learning for Human Pose Estimation

4. Determining embedding dimension for phase-space reconstruction using a geometrical construction

5. Chaotic Invariants for Human Action Recognition

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