Collaborative Research: Automatic Behavior Monitoring for In-depth Analysis of Construction Fatalities and Injuries

合作研究：自动行为监测，深入分析施工伤亡情况

• 批准号: 1200120
• 负责人: Feniosky Pena-Mora
• 金额: $ 6.83万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1200120&HistoricalAwards=false
• 关键词: Collaborative; Research; Automatic; Behavior; Monitoring

The objective of this research is to explore computer vision-based monitoring methods, enabling the automatic and constant observation of construction workers for in-depth safety analysis. Taking into account the fact that the number of fatalities in construction remains the highest among all industries, and that approximately 80 to 90 percent of accidents are strongly associated with workers' unsafe behavior and acts, the automatic capture and systematic understanding of unsafe behavior has great potential to contribute to the reduction and prevention of injuries and fatalities in construction. Specifically, using video and image sequences, the proposed system estimates the 2D location of a human skeleton, computes the 3D location of body joints, and identifies the worker?s unsafe motions using machine learning techniques. To investigate the feasibility and potential of the proposed methods for behavior monitoring, several representative motions in traumatic (e.g., falls) and ergonomic (e.g., overexertion and repetitive motions) injuries are tested as a case study. If successful, the findings of this research could lead to the prevention of injuries and fatalities in the construction industry by providing an in-depth understanding of human behavior and actions in terms of safety. Further, the motion analysis techniques developed in this project can be applied to diverse industries (e.g., manufacturing and shipbuilding) where labor is an important resource, providing a means to automatically collect data on human behavior and thus enabling its effective understanding. In addition, the education plan (e.g., the course that deals with understanding human behavior in safety, and the workshop planned for industry professionals and students) will provide effective education of future managers and engineers, both of whom will improve safety in the US workplace. Further, female and underrepresented students will be recruited and integrated into the planned research and education activities (e.g., safety seminars and workshops and interdisciplinary research participation opportunities).

这项研究的目的是探索基于计算机视觉的监视方法，从而使建筑工人自动且不断地观察到深入的安全分析。考虑到建筑物中的死亡人数仍然是所有行业中最高的事实，并且大约80％至90％的事故与工人的不安全行为和行为密切相关，对不安全行为的自动捕获和系统理解具有巨大的潜力，可以促进减少和预防施工中的伤害和死亡。具体而言，使用视频和图像序列，提出的系统估算了人类骨架的2D位置，计算身体接头的3D位置，并使用机器学习技术来识别工人的不安全运动。为了研究所提出的行为监测方法的可行性和潜力，对创伤性（例如，跌倒）和人体工程学（例如，劳累和重复运动）的几种代表性运动进行了测试。如果成功的话，这项研究的结果可能会通过深入了解人类的行为和行动，从而导致建筑行业的伤害和死亡。此外，该项目中开发的运动分析技术可以应用于劳动力是一种重要资源的各种行业（例如制造和造船），提供了一种自动收集有关人类行为的数据的方法，从而实现了其有效的理解。此外，教育计划（例如，涉及了解人类安全行为的课程以及为行业专业人士和学生计划的研讨会）将为未来的经理和工程师提供有效的教育，他们都将改善美国工作场所的安全。此外，将招募女性和代表性不足的学生，并将其纳入计划的研究和教育活动（例如，安全研讨会和讲习班以及跨学科的研究参与机会）。