Automated Vision-Based Sensing for Site Operations Analysis

用于现场操作分析的基于视觉的自动化传感

• 批准号: 1030472
• 负责人: Patricio Vela
• 金额: $ 30万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1030472&HistoricalAwards=false
• 关键词: Automated; Vision; Based; Sensing; Site

This research seeks to prove that it is possible to reliably and automatically track work progress and multiple resources with images (video and/or time-lapse) in order to reproduce the daily workflow activities associated to a construction worksite. The task of measuring the progress of construction site activities that involve workers, large machines, and materials, has often been a subjective and intensive manual process that is prone to error and, in real operations, frequently out-of-date. Demonstrating that an active vision system can effectively analyze and assess work-site progress will assist project managers by reducing the time spent monitoring and interpreting project status and performance, thus enabling increased attention to control of cost and schedule. By making project management and workforce more aware of the performance status of their project and their work environment, potential savings to the industry are envisioned. The track data will be interpreted and used to provide understanding of the spatio-temporal evolution of a worksite for automatically generating knowledge about worksite operations. In an information-based framework, much effort is spent acquiring and interpreting information. In a knowledge-based framework, efforts are allocated to making decisions based on the interpreted information.If successful, this research will transform the review and management of construction operations from being information-based to knowledge-based, thus saving human resources and improving decision effectiveness. This research has broader appeal beyond construction. Research domains incorporating or requiring vision-based sensing, diverse resources (people, small to heavy machinery, goods, etc.), and processing of the visual data for awareness of operations and activities are additional investigation domains. Examples include airport ground operations and mining operations. Contributions are also expected in the fields of machine learning and computer vision. The proposed research will impact research into site operations by enabling the automated monitoring and tracking of site resources. Video-based monitoring and processing algorithms provide a non-intrusive, easy, and, rapid mechanism for generating a body of operational information and knowledge which, when made available, will enable inquiry into construction operations that is currently not possible. Longer term, this research will serve as a valuable aid to project management by enabling tighter control and greater efficiency. By making project management and workforce more aware of the performance status of their project and their work environment, potential savings to the construction and other industries are envisioned. This research will also actively include and drive the education of the next generation of engineers (civil, electrical, and computational engineering) and construction labor pool. The research has a dedicated outreach plan to involve in this research a broad spectrum of students from high schools and industry professionals who are interested in advanced hard- and software technology

这项研究试图证明，可以通过图像（视频和/或时间段）自动自动跟踪工作进度以及多个资源，以便重现与建筑工作现场相关的日常工作流程活动。 衡量涉及工人，大型机器和材料的建筑工地活动进度的任务通常是一个主观且密集的手动过程，容易出错，并且在实际操作中经常过时。证明积极的视力系统可以有效地分析和评估工作场所的进度将通过减少监视和解释项目状态和绩效的时间来帮助项目经理，从而增加对控制成本和时间表的关注。通过使项目管理和劳动力更加了解其项目的绩效状况和工作环境，可以预见到该行业的潜在节省。轨道数据将被解释并用于提供对工作场所的时空演化的理解，以自动生成有关工作场所操作的知识。在基于信息的框架中，花费了很多努力来获取和解释信息。在基于知识的框架中，将努力分配给基于解释的信息做出决策。如果成功，这项研究将从基于信息的基于知识的审查和管理中，从而节省人力资源并提高决策有效性。 这项研究超出了建设以外的更大吸引力。研究领域包括或需要基于视觉的传感，多样化的资源（人员，小型机械，商品等）以及对视觉数据进行处理以了解操作和活动的处理领域是其他调查领域。例子包括机场地面运营和采矿业务。在机器学习和计算机视觉领域也有望做出贡献。拟议的研究将通过实现自动监控和跟踪现场资源的研究来影响对现场运营的研究。基于视频的监视和处理算法提供了一种非侵入性，简单且快速的机制，可生成一系列操作信息和知识，并在可用时，可以对目前无法进行的施工操作进行查询。从长远来看，这项研究将通过实现更严格的控制和提高效率来为项目管理提供宝贵的帮助。通过使项目管理和劳动力更加了解其项目的绩效状况和工作环境，可以预见到建筑和其他行业的潜在节省。这项研究还将积极包括并推动下一代工程师（民用，电气和计算工程）和建筑劳动池的教育。这项研究制定了一项专门的外展计划，可以参与这项研究