Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition

Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition

Skeleton-sequence-based behavior recognition models are characterized by fast processing speeds, low computational requirements, and simple structures. Graph convolutional networks (GCNs) have advantages in processing skeleton sequence data. However, existing miner behavior recognition models based...

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Bibliographic Details
Main Authors: WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo
Format: Article
Language:zho
Published: Editorial Department of Industry and Mine Automation 2024-11-01
Series:Gong-kuang zidonghua
Subjects:
miner behavior recognition
human keypoint extraction
skeleton sequence
graph convolution
lightweight pose estimation network
feature fusion
multi-dimensional feature fusion attention module
Online Access:http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024090059
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http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024090059

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