A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Vision-Based Excavator Activity Recognition and Productivity Analysis in Construction
Equipment activity recognition plays an important role in automating the analysis of onsite construction productivity, considering that human observation is always labor-intensive and time-consuming. Recently, several methods have been proposed to recognize the activity of construction equipment from videos. However, one of their common issues is the failure to conduct the recognition in a long video for classifying a sequence of equipment activities. This paper proposes a novel equipment recognition method with the support of a three-dimensional (3D) convolutional neural network. The network could extract both temporal and spatial information of the equipment to recognize its activities in a long video. The recognition results are further compiled and analyzed to identify the equipment productivity. The proposed method was tested to recognize the excavator activities (digging, swinging, and loading) on real construction sites. The results showed that the method outperformed existing equipment activity recognition methods in terms of accuracy and robustness.
Vision-Based Excavator Activity Recognition and Productivity Analysis in Construction
Equipment activity recognition plays an important role in automating the analysis of onsite construction productivity, considering that human observation is always labor-intensive and time-consuming. Recently, several methods have been proposed to recognize the activity of construction equipment from videos. However, one of their common issues is the failure to conduct the recognition in a long video for classifying a sequence of equipment activities. This paper proposes a novel equipment recognition method with the support of a three-dimensional (3D) convolutional neural network. The network could extract both temporal and spatial information of the equipment to recognize its activities in a long video. The recognition results are further compiled and analyzed to identify the equipment productivity. The proposed method was tested to recognize the excavator activities (digging, swinging, and loading) on real construction sites. The results showed that the method outperformed existing equipment activity recognition methods in terms of accuracy and robustness.
Vision-Based Excavator Activity Recognition and Productivity Analysis in Construction
Chen, Chen (author) / Zhu, Zhenhua (author) / Hammad, Amin (author) / Ahmed, Walid (author)
ASCE International Conference on Computing in Civil Engineering 2019 ; 2019 ; Atlanta, Georgia
Computing in Civil Engineering 2019 ; 241-248
2019-06-13
Conference paper
Electronic Resource
English
Vision-Based Excavator Activity Recognition and Productivity Analysis in Construction
| British Library Conference Proceedings | 2019
CONSTRUCTION AND TESTING OF THE MEASUREMENT SYSTEM FOR EXCAVATOR PRODUCTIVITY
| DOAJ | 2019