PFI-TT: Behavioral Analysis for Safer Communities: Fair and Ethical AI for Trusted Surveillance

PFI-TT：行为分析，打造更安全的社区：公平且符合道德的 AI，实现可信监控

• 批准号: 2329816
• 负责人: Hamed Tabkhi
• 金额: $ 55万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329816&HistoricalAwards=false
• 关键词: PFI; TT; Behavioral; Analysis; Safer

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project lies in its potential to revolutionize surveillance systems and promote public safety while protecting privacy. By leveraging recent advances in Artificial Intelligence (AI), the project aims to detect real-time public safety threats by only focusing on behaviors and utilizing the existing surveillance cameras. This innovation addresses the pressing challenges of rising criminal activities and public safety threats in public spaces and private businesses. By focusing on behavioral abnormalities rather than individual identification, this project helps to remove biases and promote social equity. The proposed technology has a significant potential for commercialization, with applications in various sectors, including public agencies, private businesses, and critical infrastructure, enhancing security and improving public well-being. The project will foster training and leadership development in innovation and entrepreneurship by involving students and post-docs in meetings with stakeholders, attending industry events, and collaborating closely with the industries involved. The proposed project aims to address the problem of inefficient and costly security measures by developing an innovative deep learning-based surveillance system. The project's successful implementation will foster the scientific and technological understanding of computer vision and deep learning, advancing the capabilities of surveillance systems and promoting innovation in the security industry. The project seeks to create a deep learning system capable of detecting behavioral anomalies in real-time by utilizing transformer-based architectures and identity-neutral visual feature embedding. The research objectives include analyzing complex human behavior without relying on personally identifiable information, developing a scalable technology, and conducting real-world pilots. The project aims to establish realistic metrics for evaluating detection reliability and resilience in real-world settings by integrating state-of-the-art AI advancements. Anticipated technical results include a novel anomaly detection dataset, a semi-supervised transformer-based video sequence learning approach and anomaly detection algorithm, and identity-neutral visual feature embedding advancements. The project's outcomes build upon previous NSF-funded research and will contribute to the scientific understanding of AI in surveillance applications.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.

该创新合作伙伴关系 - 技术翻译 (PFI-TT) 项目更广泛的影响/商业潜力在于其有可能彻底改变监控系统并在保护隐私的同时促进公共安全。通过利用人工智能（AI）的最新进展，该项目旨在仅关注行为并利用现有的监控摄像头来检测实时公共安全威胁。这项创新解决了公共场所和私营企业日益增多的犯罪活动和公共安全威胁所面临的紧迫挑战。通过关注行为异常而不是个人识别，该项目有助于消除偏见并促进社会公平。拟议的技术具有巨大的商业化潜力，可应用于公共机构、私营企业和关键基础设施等各个领域，从而增强安全性并改善公共福祉。该项目将通过让学生和博士后参加与利益相关者的会议、参加行业活动以及与相关行业密切合作，促进创新和创业方面的培训和领导力发展。拟议项目旨在通过开发基于深度学习的创新监控系统来解决安全措施效率低下且成本高昂的问题。该项目的成功实施将促进对计算机视觉和深度学习的科学技术理解，提高监控系统的能力并促进安全行业的创新。该项目旨在创建一个能够利用基于变压器的架构和身份中立的视觉特征嵌入实时检测行为异常的深度学习系统。研究目标包括在不依赖个人身份信息的情况下分析复杂的人类行为、开发可扩展的技术以及进行现实世界的试点。该项目旨在通过整合最先进的人工智能进步，建立现实的指标来评估现实环境中的检测可靠性和弹性。预期的技术成果包括新颖的异常检测数据集、基于半监督变压器的视频序列学习方法和异常检测算法，以及身份中立的视觉特征嵌入进步。该项目的成果建立在之前 NSF 资助的研究的基础上，将有助于对人工智能在监视应用中的科学理解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。