3D Matching of Resource Vision Tracking Trajectories

Three-dimensional (3D) paths of resources have been proposed in construction management, as an efficient way for measuring labor productivity. These paths are either extracted by using sensors such as global positioning system (GPS), radio frequency identification (RFID), and ultra-wideband (UWB), or based on cameras placed at jobsites for surveillance purposes. However, the tag-based methods are seriously limited by privacy conflicts since they are not welcome from the personnel. On the other hand, the computer vision based methods have not achieved full automation in measuring labour productivity because they require prior knowledge of the type of tasks performed in specific working zones. This is associated with the lack of depth information. For this purpose, this paper proposes a computationally efficient computer vision method for matching construction workers across different frames. Entity matching is a process that corresponds to a compulsory step prior to the calculation of the 3D position. The proposed matching method, is based on epipolar geometry, template and motion similarity features. The main result of this process is to provide a method for the acquisition of the 3D paths that compose the detailed profile of a construction activity in terms of both time and space.This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via https://doi.org/10.1061/9780784479827.17

Vision-based excavator pose estimation using synthetically generated datasets with domain randomization

The ability to monitor and track the interactions between construction equipment and workers can lead to creating a safer and more productive work environment. Most recent studies employ computer vision and deep learning techniques, which rely on the size and quality of the training datasets for optimal performance. However, preparation of large datasets with high quality annotations remains a manual and time-consuming process. To overcome this challenge, this study presents a framework for synthetically generating large and accurately annotated images. The contribution of this paper is manifold: First, a method is developed using a game engine, which employs domain randomization (DR) to produce large labelled datasets for excavator pose estimation. Second, a state-of-the-art deep learning architecture based on high representation network is adapted and modified for excavator pose estimation. This model is trained on synthetically generated datasets and its performance is evaluated. The results reveal that the model trained on synthetic data can yield comparable results to the model trained on real images of excavators. This demonstrates the effectiveness of utilizing synthetic datasets for complex vision tasks such as equipment pose estimation. The study concludes by highlighting directions for further work in synthetic data studies in construction