CPS: Medium: Collaborative Research: Active Shooter Tracking & Evacuation Routing for Survival (ASTERS)

美国大多数幼儿园至12年级的学区都投入了大量资源来防范活跃的枪手，例如，加强学校管理、社区规划、识别可疑行为、执法部门的危机培训，以及对学生、教师和所有学校人员的培训。然而，当这种主动射击事件正在进行时，学生和学校人员只能以“跑-躲-打”协议等指令的形式获得模糊的指导。主动枪手跟踪和逃生路线（ASTERS）项目将通过多个摄像头和麦克风实时跟踪枪手，为每个学生、教师和工作人员计算最佳的安全疏散路径，并通过与学生、家长、家长等相关社区合作创建的移动应用程序界面传达这些信息，从而补充这些努力。教育工作者和管理人员，以及学校资源官员和学校安全官员。aster将结合多模态传感、机器学习和信号处理技术，以准确定位枪手和武器，同时保护学校社区成员的隐私。它还将使用新的计算机视觉和高性能计算解决方案来估计人群密度和人员移动，并使用新颖的优化和实时模拟算法，根据建筑物布局和预测枪手的移动来预测理想的疏散路线。ASTERS将与学校合作，结合数字模拟数据和现实生活中的实践演练，开发一个带注释的多模态主动射击数据集。研究小组还将与第一反应者合作，以确保aster符合他们的需求。通过移动应用程序向每组平民提供定制和可操作的命令，可能会极大地提高安全撤离的机会。信息将提供明确的可操作信息和建议，例如“枪手正离开自助餐厅前往健身房。最佳出口是从正门出去”，而不是让个人惊慌失措地做出判断。此外，aster系统将能够自动、即时地向911呼叫中心报告射击者的位置、物理属性和使用的武器类型。这将为作出反应的巡逻官员提供关键的战略信息，以规划战术进攻，并减轻（如果不能克服的话）对不可靠的目击者叙述的依赖。以往大规模枪击事件的数据表明，向人们提供准确的疏散信息和指导非常重要。该项目将实现集成传感器、通信、算法和人为因素研究的智能安全系统，为弱势群体提供救生信息。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Most preK-12 school districts in the United States dedicate significant resources to safeguard against active shooters, e.g., school hardening, community planning, identification of suspicious behavior, crisis training for law enforcement, and training exercises for students, teachers, and all school personnel. However, when such an active-shooting event is in progress, only vague guidance is available to students and school personnel in the form of directives such as the "run-hide-fight" protocol. The Active Shooter Tracking and Evacuation Routing for Survival (ASTERS) project will complement these efforts by tracking a shooter in real time across multiple cameras and microphones, calculate the optimum evacuation path to safety for each student, teacher, and staff member, and communicate this information through a mobile app interface that is co-created in partnership with a connected community of students, parents, educators and administrators as well as school resource officers and school safety officers. ASTERS will incorporate multi-modal sensing, machine learning and signal processing techniques to accurately localize a gunman and weapons while preserving privacy of school community members. It will also use new computer vision and high-performance computing solutions to estimate crowd density and movement of people, and novel optimization and real-time simulation algorithms to predict ideal evacuation routes based on the building layout and predicted movement of the shooter. ASTERS will collaborate with schools to develop an annotated, multi-modal active shooter data set using a combination of digital simulation data and real-life practice drills. The research team will also partner with first-responders to ensure that ASTERS aligns with their needs. Providing customized and actionable commands to each group of civilians through a mobile app will potentially vastly improve chances of safe evacuation. Messages will provide clear actionable information and suggestions, such as "Shooter is leaving the cafeteria heading to the gym. Your best exit is out the Main Entrance", rather than leave it up to individuals' panicked judgement. Moreover, ASTERS will enable automated and instantaneous reporting of location and physical attributes of shooter and type of weapons being used, to a 911 call center. This will provide responding patrol officers with critical strategic information for planning a tactical offensive and alleviate, if not overcome, the dependence on unreliable eye-witness accounts. Data from previous mass shootings demonstrate the important of providing people accurate information and guidance about evacuation. The ASTERS project will enable the realization of smart safety systems that integrate sensors, communication, algorithms, and human factors research to provide life-saving information to vulnerable people.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.