RAPID: A Smart and Mobile Sensor Fusion Framework for Earthquake Hazard Reduction, Situational Assessment, and Relief Efforts

RAPID：用于减少地震灾害、态势评估和救灾工作的智能移动传感器融合框架

• 批准号: 1942053
• 负责人: Karen Panetta
• 金额: $ 9.85万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942053&HistoricalAwards=false
• 关键词: RAPID; Smart; Mobile; Sensor; Fusion

Natural disasters including earthquakes, tsunamis, hurricanes and anthropogenic disasters, such as wildfires, are dynamic situations requiring constant monitoring as numerous hazards are constantly emerging that hinder humanitarian efforts and create deadly conditions for rescue workers and victims. Most disaster area assessment, and search and rescue efforts, rely heavily on visual imagery captured from cell phones, aerial video and other forms of media. Currently, real-time assessment for disasters depends entirely on the human operator's ability to visually identify the subject of interest in the video, or images captured. Further, damaged buildings and roadways, debris, smoke, fire, and environmental conditions such as rain complicate the human observer's ability to monitor situations for detecting victims and hazards. It is essential that rescuers can assess hazardous conditions before risking their lives and be armed with smart technologies that enable them to respond to dynamic situations. The recent events in Southern California (earthquakes) and Louisiana (Hurricane Barry) provide perishable data that will enable maturation of the proposed research activities. This plus the integration within the Smart Mobile Disaster Data Collection unit for response provides further justification of the RAPID award mechanism. The researchers will create a Smart Mobile Disaster Data Collection and Assessment tool for detecting and mapping out situational hazards using intelligent data fused information collected from multiple sensor technologies. Furthermore, this project aims to create a first-of-its-kind public database of collected imagery and media from disasters to help researchers and agencies share information or to assist in developing best practices and collaborative strategies. This project will also enable better visualization of disparate sources of data, thus making it conducive for human operator to detect hazards and victims. This research will also enhance safety and security applications across a wide range of areas including firefighting, disaster relief, and search-and-rescue. The approach of seamlessly utilizing and visualizing sensor information in a situational map to guide responders' actions and enhance their efficiency reduces the risk factor for responders.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.

包括地震、海啸、飓风在内的自然灾害和野火等人为灾害是动态的，需要不断监测，因为不断出现的许多危害阻碍了人道主义工作，并为救援人员和受害者创造了致命的条件。大多数灾区评估以及搜索和救援工作在很大程度上依赖于从手机、空中视频和其他形式的媒体捕获的视觉图像。目前，灾害的实时评估完全取决于操作人员在视频或捕获的图像中视觉识别感兴趣主题的能力。此外，受损的建筑物和道路、碎片、烟雾、火灾和环境条件（例如雨）使人类观察者监测用于检测受害者和危险的情况的能力复杂化。至关重要的是，救援人员可以在冒着生命危险之前评估危险情况，并配备智能技术，使他们能够应对动态情况。 最近发生在南加州（地震）和路易斯安那州（飓风巴里）的事件提供了易逝的数据，将使拟议的研究活动成熟。 这一点，加上与用于应对的智能移动的灾害数据收集单位的整合，进一步证明了RAPID奖励机制的合理性。研究人员将创建一个智能移动的灾害数据收集和评估工具，用于使用从多种传感器技术收集的智能数据融合信息来检测和绘制情景危险。此外，该项目旨在建立一个首个收集灾害图像和媒体的公共数据库，以帮助研究人员和机构分享信息或协助制定最佳做法和合作战略。该项目还将使不同来源的数据更好地可视化，从而有利于人类操作员检测危险和受害者。这项研究还将加强消防、救灾和搜救等广泛领域的安全和安保应用。通过在情景地图中无缝利用和可视化传感器信息，指导响应人员的行动并提高其效率，从而降低了响应人员的风险系数。该奖项反映了NSF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。

项目成果

Comprehensive Underwater Object Tracking Benchmark Dataset and Underwater Image Enhancement With GAN

• DOI: 10.1109/joe.2021.3086907
• 发表时间: 2021-07-27
• 期刊: IEEE JOURNAL OF OCEANIC ENGINEERING
• 影响因子: 4.1
• 作者: Panetta，Karen;Kezebou，Landry;Agaian，Sos
• 通讯作者: Agaian，Sos

Face description using anisotropic gradient: thermal infrared to visible face recognition

使用各向异性梯度的面部描述：热红外到可见光面部识别

• DOI: 10.1117/12.2304898
• 发表时间: 2018
• 期刊: and Applications 2018
• 作者: Wan, Qianwen;Rao, Shishir Paramathma;Panetta, Karen;Agaian, Sos S.;Kaszowska, Aleksandra;Taylor, Holly;Voronina, V.
• 通讯作者: Voronina, V.

Quaternion based neural network for hyperspectral image classification

基于四元数的神经网络用于高光谱图像分类

• DOI: 10.1117/12.2558808
• 发表时间: 2020
• 期刊: and Applications 2020
• 作者: Rao, Shishir Paramathma;Panetta, Karen;Agaian, Sos S.
• 通讯作者: Agaian, Sos S.

Augmented reality-based vision-aid indoor navigation system in GPS denied environment

GPS 缺失环境下基于增强现实的视觉辅助室内导航系统

• DOI: 10.1117/12.2519224
• 发表时间: 2019
• 期刊: and Applications
• 作者: Rajeev, Srijith;Wan, Qianwen;Yau, Kenny;Panetta, Karen;Agaian, Sos S.
• 通讯作者: Agaian, Sos S.

Aerial Border Surveillance for Search and Rescue Missions Using Eye Tracking Techniques

• DOI: 10.1109/ths.2018.8574165
• 发表时间: 2018-10
• 期刊: 2018 IEEE International Symposium on Technologies for Homeland Security (HST)
• 作者: Qianwen Wan;Aleksandra Kaszowska;A. Samani;K. Panetta;H. Taylor;S. Agaian