Automated Organization ProfileLaing O'Rourke Reader, Department of Engineering, University of Cambridge
Laing O'Rourke Reader, Department of Engineering, University of Cambridge
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.2 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Data set used for testing the efficiency of a vision-based tracking method on tracking construction workers.
Authors
- Konstantinou, Eirini ;
- Lasenby, Joan ;
- Brilakis, Ioannis
Data set used for testing the efficiency of a vision-based tracking method on tracking construction workers.
Authors
- Konstantinou, Eirini ;
- Lasenby, Joan ;
- Brilakis, Ioannis
Computer vision based tracking of construction resources is one of the several options available for obtaining trajectories useful in safety and productivity applications. This type of tracking requires that targets are accurately matched across multiple camera views to obtain a 3D trajectory out of two or more 2D trajectories. This matching is straightforward when it involves easily distinguishable targets in uncluttered scenes. This can be challenging in industrial scenes such as construction sites due to congestion, occlusions and workers in greatly similar high visibility apparel. This paper proposes a novel vision based method that addresses all these issues. It uses as input the output of a 2D vision based tracking method and searches for potential matches in three sequential steps. It terminates only when a positive match is found. The first step returns the strongest candidate by correlating a segment of workers’ past 2D trajectories. The second employs geometric restrictions, whilst the third correlates colour intensity values. The proposed method featured a promising performance of 97% precision, 98% recall and 95% accuracy.
Authors
- Konstantinou, Eirini ;
- Brilakis, Ioannis
Computer vision based tracking of construction resources is one of the several options available for obtaining trajectories useful in safety and productivity applications. This type of tracking requires that targets are accurately matched across multiple camera views to obtain a 3D trajectory out of two or more 2D trajectories. This matching is straightforward when it involves easily distinguishable targets in uncluttered scenes. This can be challenging in industrial scenes such as construction sites due to congestion, occlusions and workers in greatly similar high visibility apparel. This paper proposes a novel vision based method that addresses all these issues. It uses as input the output of a 2D vision based tracking method and searches for potential matches in three sequential steps. It terminates only when a positive match is found. The first step returns the strongest candidate by correlating a segment of workers’ past 2D trajectories. The second employs geometric restrictions, whilst the third correlates colour intensity values. The proposed method featured a promising performance of 97% precision, 98% recall and 95% accuracy.
Authors
- Konstantinou, Eirini ;
- Brilakis, Ioannis