TY - JOUR
T1 - Crowdsourcing Construction Activity Analysis from Jobsite Video Streams
AU - Liu, Kaijian
AU - Golparvar-Fard, Mani
N1 - Publisher Copyright:
© 2015 American Society of Civil Engineers.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - 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.
AB - 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.
KW - Activity analysis
KW - Construction productivity
KW - Crowdsourcing
KW - Information technologies
KW - Video-based monitoring
KW - Workface assessment
UR - http://www.scopus.com/inward/record.url?scp=84945581506&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84945581506&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)CO.1943-7862.0001010
DO - 10.1061/(ASCE)CO.1943-7862.0001010
M3 - Article
AN - SCOPUS:84945581506
SN - 0733-9364
VL - 141
JO - Journal of Construction Engineering and Management
JF - Journal of Construction Engineering and Management
IS - 11
M1 - 04015035
ER -