首页 | 本学科首页   官方微博 | 高级检索  
     


Construction performance monitoring via still images,time-lapse photos,and video streams: Now,tomorrow, and the future
Affiliation:1. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States;2. Electrical and Electronic Engineering Department, Universidad del Norte, Barranquilla, Colombia;3. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N. Mathews Ave., Urbana, IL 61801, United States;4. Department of Computer Science, University of Illinois at Urbana-Champaign, 205 N. Mathews Ave., Urbana, IL 61801, United States;1. Department of Civil and Environmental Engineering, Myongji University, 4th Engineering Building, 116 Myongji-ro, Yongin, Gyeonggi-do 449-728, South Korea;2. Department of Engineering, University of Cambridge, BC2-07, Trumpington Street, Cambridge CB2 1PZ, UK
Abstract:Timely and accurate monitoring of onsite construction operations can bring an immediate awareness on project specific issues. It provides practitioners with the information they need to easily and quickly make project control decisions. Despite their importance, the current practices are still time-consuming, costly, and prone to errors. To facilitate the process of collecting and analyzing performance data, researchers have focused on devising methods that can semi-automatically or automatically assess ongoing operations both at project level and operation level. A major line of work has particularly focused on developing computer vision techniques that can leverage still images, time-lapse photos and video streams for documenting the work in progress. To this end, this paper extensively reviews these state-of-the-art vision-based construction performance monitoring methods. Based on the level of information perceived and the types of output, these methods are mainly divided into two categories (namely project level: visual monitoring of civil infrastructure or building elements vs. operation level: visual monitoring of construction equipment and workers). The underlying formulations and assumptions used in these methods are discussed in detail. Finally the gaps in knowledge that need to be addressed in future research are identified.
Keywords:Construction  Performance monitoring  Computer vision  Machine learning
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号