|
Abstract:
|
After a century of sporadic advances in equipment , tools , materials , and methods , the US construction industry still faces a low rate of productivity growth . To improve the productivity of any site activity , it is important to rapidly record relevant data about utilized resources and processes , as well as about the output quantities produced by these activities . There is sufficient evidence to suggest that activity -level productivity measurement is the premise for making any productivity improvement decision . To date , certain aspects of productivity measurement , such as input /output quantities , are partially automated through advanced project control systems . However , measuring the process of construction activities for productivity improvement remains an elusive goal for most construction companies . This is mostly due to the massive manual effort embedded in these data collection methods . Digital cameras are inexpensive devices that are widely used in the construction industry as an effective site observation method . This opens the door for conducting scientific method studies on complex operations through examining recorded videos . However , in the absence of an efficient video interpretation method , tedious manual reviewing is currently still required to extract productivity information from the recorded videos . This research aims to develop a computational methodology to rapidly and intelligently interpret construction videos into productivity information . It determines what elements can represent the steps and information flows in construction video interpretation . It identifies , develops , and evaluates computer vision algorithms to enable reliable visual recognition and tracking of construction resources in typical construction environments . It develops methods to enable context aware video computing . A software prototype , the Construction Video Analyzer , was developed and implemented based on this conceptual methodology . The proposed methodology was validated through using the developed prototype system to analyze five construction video sequences that record various types of construction operations . The Construction Video Analyzer was able to interpret these videos into productivity information with an accuracy that was close to manual analysis , without the limitations of onsite human observation . The developed methodology provides site management with a tool that can rapidly collect productivity data with greatly reduced manual efforts . |