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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number9350
Journal / PublicationSensors
Volume23
Issue number23
Online published23 Nov 2023
Publication statusPublished - Dec 2023

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Abstract

The global concern regarding the monitoring of construction workers’ activities necessitates an efficient means of continuous monitoring for timely action recognition at construction sites. This paper introduces a novel approach—the multi-scale graph strategy—to enhance feature extraction in complex networks. At the core of this strategy lies the multi-feature fusion network (MF-Net), which employs multiple scale graphs in distinct network streams to capture both local and global features of crucial joints. This approach extends beyond local relationships to encompass broader connections, including those between the head and foot, as well as interactions like those involving the head and neck. By integrating diverse scale graphs into distinct network streams, we effectively incorporate physically unrelated information, aiding in the extraction of vital local joint contour features. Furthermore, we introduce velocity and acceleration as temporal features, fusing them with spatial features to enhance informational efficacy and the model’s performance. Finally, efficiency-enhancing measures, such as a bottleneck structure and a branch-wise attention block, are implemented to optimize computational resources while enhancing feature discriminability. The significance of this paper lies in improving the management model of the construction industry, ultimately aiming to enhance the health and work efficiency of workers. © 2023 by the authors.

Research Area(s)

  • 3D skeleton data, construction worker action recognition, deep learning algorithm, multi-stream network

Citation Format(s)

Multi-Stream Fusion Network for Skeleton-Based Construction Worker Action Recognition. / Tian, Yuanyuan; Liang, Yan; Yang, Haibin et al.
In: Sensors, Vol. 23, No. 23, 9350, 12.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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