Using synthetic data to improve and evaluate the tracking performance of construction workers on site
- Vision-based tracking systems enable the optimization of the productivity and safety management on construction sites by monitoring the workers’ movements. However, training and evaluation of such a system requires a vast amount of data. Sufficient datasets rarely exist for this purpose. We investigate the use of synthetic data to overcome this issue. Using 3D computer graphics software, we model virtual construction site scenarios. These are rendered for the use as a synthetic dataset which augments a self-recorded real world dataset. Our approach is verified by means of a tracking system. For this, we train a YOLOv3 detector identifying pedestrian workers. Kalman filtering is applied to the detections to track them over consecutive video frames. First, the detector’s performance is examined when using synthetic data of various environmental conditions for training. Second, we compare the evaluation results of our tracking system on real world and synthetic scenarios. With an increase of about 7.5 percentage points in mean average precision, our findings show that a synthetic extension is beneficial for otherwise small datasets. The similarity of synthetic and real world results allow for the conclusion that 3D scenes are an alternative to evaluate vision-based tracking systems on hazardous scenes without exposing workers to risks.
Author: | Marcel NeuhausenORCiDGND, Patrick HerbersORCiDGND, Markus KönigORCiDGND |
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URN: | urn:nbn:de:hbz:294-78123 |
DOI: | https://doi.org/10.13154/294-7812 |
Parent Title (English): | Applied sciences |
Publisher: | MDPI |
Place of publication: | Basel |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2021/02/01 |
Date of first Publication: | 2020/07/18 |
Publishing Institution: | Ruhr-Universität Bochum, Universitätsbibliothek |
Tag: | Deep learning; Open Access Fonds construction productivity; construction safety; synthetic data; tracking |
Volume: | 10 |
Issue: | 14, Artikel 4948 |
First Page: | 4948-1 |
Last Page: | 4948-18 |
Note: | Article Processing Charge funded by the Deutsche Forschungsgemeinschaft (DFG) and the Open Access Publication Fund of Ruhr-Universität Bochum. |
Institutes/Facilities: | Lehrstuhl für Informatik im Bauwesen |
Dewey Decimal Classification: | Allgemeines, Informatik, Informationswissenschaft / Informatik |
open_access (DINI-Set): | open_access |
Licence (English): | Creative Commons - CC BY 4.0 - Attribution 4.0 International |