A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fast Dataset Collection Approach for Articulated Equipment Pose Estimation
Struck-by accidents are potential safety concerns on construction sites and require a robust machine pose estimation. The development of deep learning methods has enhanced the human pose estimation that can be adapted for articulated machines. These methods require abundant dataset for training, which is challenging and time-consuming to obtain on-site. This paper proposes a fast data collection approach to build the dataset for excavator pose estimation. It uses two industrial robot arms as the excavator and the camera monopod to collect different excavator pose data. The 3D annotation can be obtained from the robot’s embedded encoders. The 2D pose is annotated manually. For evaluation, 2,500 pose images were collected and trained with the stacked hourglass network. The results showed that the dataset is suitable for the excavator pose estimation network training in a controlled environment, which leads to the potential of the dataset augmenting with real construction site images.
Fast Dataset Collection Approach for Articulated Equipment Pose Estimation
Struck-by accidents are potential safety concerns on construction sites and require a robust machine pose estimation. The development of deep learning methods has enhanced the human pose estimation that can be adapted for articulated machines. These methods require abundant dataset for training, which is challenging and time-consuming to obtain on-site. This paper proposes a fast data collection approach to build the dataset for excavator pose estimation. It uses two industrial robot arms as the excavator and the camera monopod to collect different excavator pose data. The 3D annotation can be obtained from the robot’s embedded encoders. The 2D pose is annotated manually. For evaluation, 2,500 pose images were collected and trained with the stacked hourglass network. The results showed that the dataset is suitable for the excavator pose estimation network training in a controlled environment, which leads to the potential of the dataset augmenting with real construction site images.
Fast Dataset Collection Approach for Articulated Equipment Pose Estimation
Liang, Ci-Jyun (author) / Lundeen, Kurt M. (author) / McGee, Wes (author) / Menassa, Carol C. (author) / Lee, SangHyun (author) / Kamat, Vineet R. (author)
ASCE International Conference on Computing in Civil Engineering 2019 ; 2019 ; Atlanta, Georgia
Computing in Civil Engineering 2019 ; 146-152
2019-06-13
Conference paper
Electronic Resource
English
Fast Dataset Collection Approach for Articulated Equipment Pose Estimation
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