Enhancing Human Action Recognition with 3D Skeleton Data: A Comprehensive Study of Deep Learning and Data Augmentation
Human Action Recognition (HAR) is an important field that identifies human behavior through sensor data. Three-dimensional human skeleton data extracted from the Kinect depth sensor have emerged as a powerful alternative to mitigate the effects of lighting and occlusion of traditional 2D RGB or gray...
Main Authors: | Chu Xin, Seokhwan Kim, Yongjoo Cho, Kyoung Shin Park |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-02-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/13/4/747 |
Similar Items
-
A Discriminative Dual-Stream Model With a Novel Sustained Attention Mechanism for Skeleton-Based Human Action Recognition
by: Zhihong Liang, et al.
Published: (2020-01-01) -
Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder
by: Yunkai Zhang, et al.
Published: (2021-01-01) -
A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
by: Junjie Chen, et al.
Published: (2021-12-01) -
Robust Multi-Feature Learning for Skeleton-Based Action Recognition
by: Yingfu Wang, et al.
Published: (2019-01-01) -
Hierarchical long short-term memory for action recognition based on 3D skeleton joints from Kinect sensor
by: Nur Awal Hidayanto, et al.
Published: (2021-01-01)