Convolutional neural networks: Computer vision-based workforce activity assessment in construction
Computer vision approaches have been widely used to automatically recognize the activities of workers from videos. While considerable advancements have been made to capture complementary information from still frames, it remains a challenge to obtain motion between them. As a result, this has hinder...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Elsevier BV
2018
|
| Online Access: | http://hdl.handle.net/20.500.11937/70025 |
| _version_ | 1848762195591036928 |
|---|---|
| author | Luo, H. Xiong, C. Fang, W. Love, Peter Zhang, B. Ouyang, X. |
| author_facet | Luo, H. Xiong, C. Fang, W. Love, Peter Zhang, B. Ouyang, X. |
| author_sort | Luo, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Computer vision approaches have been widely used to automatically recognize the activities of workers from videos. While considerable advancements have been made to capture complementary information from still frames, it remains a challenge to obtain motion between them. As a result, this has hindered the ability to conduct real-time monitoring. Considering this challenge, an improved convolutional neural network (CNN) that integrates Red-Green-Blue (RGB), optical flow, and gray stream CNNs, is proposed to accurately monitor and automatically assess workers’ activities associated with installing reinforcement during construction. A database containing photographs of workers installing reinforcement is created from activities undertaken on several construction projects in Wuhan, China. The database is then used to train and test the developed CNN network. Results demonstrate that the developed method can accurately detect the activities of workers. The developed computer vision-based approach can be used by construction managers as a mechanism to assist them to ensure that projects meet pre-determined deliverables. |
| first_indexed | 2025-11-14T10:43:43Z |
| format | Journal Article |
| id | curtin-20.500.11937-70025 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:43:43Z |
| publishDate | 2018 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-700252019-01-21T23:34:45Z Convolutional neural networks: Computer vision-based workforce activity assessment in construction Luo, H. Xiong, C. Fang, W. Love, Peter Zhang, B. Ouyang, X. Computer vision approaches have been widely used to automatically recognize the activities of workers from videos. While considerable advancements have been made to capture complementary information from still frames, it remains a challenge to obtain motion between them. As a result, this has hindered the ability to conduct real-time monitoring. Considering this challenge, an improved convolutional neural network (CNN) that integrates Red-Green-Blue (RGB), optical flow, and gray stream CNNs, is proposed to accurately monitor and automatically assess workers’ activities associated with installing reinforcement during construction. A database containing photographs of workers installing reinforcement is created from activities undertaken on several construction projects in Wuhan, China. The database is then used to train and test the developed CNN network. Results demonstrate that the developed method can accurately detect the activities of workers. The developed computer vision-based approach can be used by construction managers as a mechanism to assist them to ensure that projects meet pre-determined deliverables. 2018 Journal Article http://hdl.handle.net/20.500.11937/70025 10.1016/j.autcon.2018.06.007 Elsevier BV restricted |
| spellingShingle | Luo, H. Xiong, C. Fang, W. Love, Peter Zhang, B. Ouyang, X. Convolutional neural networks: Computer vision-based workforce activity assessment in construction |
| title | Convolutional neural networks: Computer vision-based workforce activity assessment in construction |
| title_full | Convolutional neural networks: Computer vision-based workforce activity assessment in construction |
| title_fullStr | Convolutional neural networks: Computer vision-based workforce activity assessment in construction |
| title_full_unstemmed | Convolutional neural networks: Computer vision-based workforce activity assessment in construction |
| title_short | Convolutional neural networks: Computer vision-based workforce activity assessment in construction |
| title_sort | convolutional neural networks: computer vision-based workforce activity assessment in construction |
| url | http://hdl.handle.net/20.500.11937/70025 |