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Batselier, J and Vanhoucke, M (2015) . Journal of Construction Engineering and Management, 141(11).

Liu, K and Golparvar-Fard, M (2015) Crowdsourcing Construction Activity Analysis from Jobsite Video Streams. Journal of Construction Engineering and Management, 141(11).

  • Type: Journal Article
  • Keywords: Activity analysis; Construction productivity; Video-based monitoring; Workface assessment; Crowdsourcing; Information technologies;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001010
  • Abstract:
    The advent of affordable jobsite cameras is reshaping the way on-site construction activities are monitored. To facilitate the analysis of large collections of videos, research has focused on addressing the problem of manual workface assessment by recognizing worker and equipment activities using computer-vision algorithms. Despite the explosion of these methods, the ability to automatically recognize and understand worker and equipment activities from videos is still rather limited. The current algorithms require large-scale annotated workface assessment video data to learn models that can deal with the high degree of intraclass variability among activity categories. To address current limitations, this study proposes crowdsourcing the task of workface assessment from jobsite video streams. By introducing an intuitive web-based platform for massive marketplaces such as Amazon Mechanical Turk (AMT) and several automated methods, the intelligence of the crowd is engaged for interpreting jobsite videos. The goal is to overcome the limitations of the current practices of workface assessment and also provide significantly large empirical data sets together with their ground truth that can serve as the basis for developing video-based activity recognition methods. Six extensive experiments have shown that engaging nonexperts on AMT to annotate construction activities in jobsite videos can provide complete and detailed workface assessment results with 85% accuracy. It has been demonstrated that crowdsourcing has the potential to minimize time needed for workface assessment, provides ground truth for algorithmic developments, and most importantly allows on-site professionals to focus their time on the more important task of root-cause analysis and performance improvements.

Love, P E D, Ackermann, F, Teo, P and Morrison, J (2015) From Individual to Collective Learning: A Conceptual Learning Framework for Enacting Rework Prevention. Journal of Construction Engineering and Management, 141(11).

Neuman, Y, Alves, T d C L, Walsh, K D and Needy, K L (2015) Quantitative Analysis of Supplier Quality Surveillance Practices in EPC Projects. Journal of Construction Engineering and Management, 141(11).

Shen, Y, Koh, T Y, Rowlinson, S and Bridge, A J (2015) Empirical Investigation of Factors Contributing to the Psychological Safety Climate on Construction Sites. Journal of Construction Engineering and Management, 141(11).

Sing, M C P, Edwards, D J, Liu, H J X and Love, P E D (2015) Forecasting Private-Sector Construction Works: VAR Model Using Economic Indicators. Journal of Construction Engineering and Management, 141(11).

Su, Y and Lucko, G (2015) Optimum Present Value Scheduling Based on Synthetic Cash Flow Model with Singularity Functions. Journal of Construction Engineering and Management, 141(11).