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Multi-Stream Fusion Network for Skeleton-Based Construction Worker Action Recognition.

Yuanyuan Tian1, Yan Liang2, Haibin Yang2

  • 1Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-scale graph strategy and a multi-feature fusion network (MF-Net) for enhanced construction worker activity monitoring. This approach improves worker health and efficiency through better action recognition.

Keywords:
3D skeleton dataconstruction worker action recognitiondeep learning algorithmmulti-stream network

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Continuous monitoring of construction worker activities is crucial for safety and efficiency.
  • Existing methods struggle with complex spatial-temporal relationships in human actions.

Purpose of the Study:

  • To develop an efficient and accurate system for construction worker action recognition.
  • To enhance feature extraction in complex human pose networks for construction sites.

Main Methods:

  • Introduced a multi-scale graph strategy for enhanced feature extraction.
  • Developed a multi-feature fusion network (MF-Net) using distinct network streams for local and global features.
  • Incorporated temporal features (velocity, acceleration) fused with spatial features.
  • Implemented efficiency measures like bottleneck structure and branch-wise attention.

Main Results:

  • The MF-Net effectively captures both local joint features and broader human body connections.
  • Fusion of spatial and temporal features significantly improved model performance.
  • Efficiency-enhancing measures optimized computational resources and feature discriminability.

Conclusions:

  • The proposed multi-scale graph strategy and MF-Net offer a novel and effective approach to construction worker activity monitoring.
  • This technology has the potential to significantly improve construction site management, worker health, and overall work efficiency.