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Cited by 202

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Automated excavators activity recognition and productivity analysis from construction site surveillance videos

Chen, Chen, Zhu, Zhenhua, Hammad, Amin·2020·Automation in Construction
DOI 10.1016/j.autcon.2019.103045
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Abstract

Abstract The productivity of construction equipment plays an important role in completing construction projects within schedule and under budget. However, the current productivity monitoring on construction sites highly depends on manually observing and recording equipment activities, which is labor-intensive and time-consuming. To address this problem, an increasing number of research studies focused on automatically identifying equipment activities from site surveillance videos. However, these studies failed to accurately conduct the activity recognition and productivity analysis, when multiple pieces of equipment are working together. This research proposes a novel framework for automatically analyzing the activity and productivity of multiple excavators. In this framework, three convolutional neural networks are designed to detect, track and recognize the activities of excavators. The results are further compiled to analyze excavator's activity time, working cycle, and productivity. The proposed framework has been tested with the videos recorded from real construction sites. The overall activity recognition has achieved 87.6% accuracy. The productivity calculation has achieved 83% accuracy, which indicates the feasibility of the proposed framework for automating the monitoring of excavator's productivity.

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Metadata

Authors
Chen, C., Zhu, Z., & Hammad, A.
Venue
Automation in Construction
Publisher
Elsevier BV
Published
2020-02
Volume
110
Pages
103045
ISSN
0926-5805 (ISSN-L)
DOI
10.1016/j.autcon.2019.103045
OpenAlex
W2994999390
Cited by
202
References
2
FWCI
10.68
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
Mitacs

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CoChat. (2026). Citation check: Automated excavators activity recognition and productivity analysis from construction site surveillance videos. https://app.cochat.ai/papers/10.1016/j.autcon.2019.103045
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Chen, C., Zhu, Z., & Hammad, A. (2020). Automated excavators activity recognition and productivity analysis from construction site surveillance videos. Automation in Construction. https://doi.org/10.1016/j.autcon.2019.103045

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