An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations

Measuring the process of construction operations for productivity improvement remains a difficult task for most construction companies due to the manual effort required in most activity measurement methods. This paper proposed and described the elements, processes, and algorithms that comprise a computational and intelligent construction video interpretation method. A number of vision-based construction object recognition and tracking methods were evaluated to provide guidance for algorithm selection. A prototype system was developed to integrate the proposed video analysis processes and selected computer vision algorithms. Videos of construction operations were analyzed to validate the proposed method. Comparing to the traditional manual construction video analysis method, the proposed method provided a semi-automated video interpretation method. The new method enabled the interpretation of these videos into productivity information, such as working processes, cycle times, and delays, with an accuracy that was comparable to manual analysis, without the limitations of on-site human observation.

Research Highlights
► We proposed and developed a computational construction video interpretation method for rapid productivity analysis of construction operations.
► We developed and evaluated several vision-based construction object recognition and tracking methods.
► We integrated computer vision algorithms with operation process modeling methods for construction video analysis.
► We developed a prototype system based on the proposed method, and tested its performance on cyclic and non-cyclic construction operations.
► Integration of computer vision algorithms with operation process modeling methods results in efficient and effective productivity analysis of construction operations.