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

这个创新-技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力在于它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，该项目旨在通过只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。这一创新解决了公共场所和私营企业中日益增加的犯罪活动和公共安全威胁的紧迫挑战。通过关注行为异常而不是个人身份，该项目有助于消除偏见和促进社会公平。拟议的技术具有巨大的商业化潜力，可应用于各个部门，包括公共机构，私营企业和关键基础设施，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，并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。该项目旨在通过开发基于深度学习的创新监控系统来解决效率低下和成本高昂的安全措施问题。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和技术理解，提高监控系统的能力，促进安全行业的创新。该项目旨在创建一个深度学习系统，能够通过利用基于transformer的架构和身份中立的视觉特征嵌入来实时检测行为异常。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，并进行真实世界的飞行员。该项目旨在通过整合最先进的人工智能技术，建立现实的指标，以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一个新的异常检测数据集，基于半监督变换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改进。该项目的成果建立在以前NSF资助的研究基础上，将有助于对人工智能在监控应用中的科学理解。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。

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.