SCC: Building Safe and Secure Communities through Real-Time Edge Video Analytics

无论是在美国还是在世界各地，智能技术的出现使社区居民与周围环境之间建立联系的新时代成为可能。随着城市地区的新一波增长，确保公共安全是迈向“智慧”城市和社区的重要前提。该项目提出了一种新颖的“智能”警务技术，作为一种变革性的解决方案，有效地加强执法，同时最大限度地减少不必要的互动并维护居民隐私。这项拟议中的技术提供了一个智能摄像头网络，不需要持续监控，而是经过训练，可以在现场实时发出警报。由于摄像头识别的是行为而不是身份，它们可以减少偏见，最大限度地减少误报，并保护个人隐私。智能警务技术将在社区居民、社区领导、当地企业主以及夏洛特市等机构以及夏洛特-梅克伦堡县和加斯顿县的当地执法机构的直接帮助下共同设计和共同创造。该研究在计算机视觉、计算机体系结构、实时边缘计算、犯罪学和社区技术交互等多个领域取得了基础性进展。它为利用深度学习和数据分析方面的最新进展，在不损害居民隐私的情况下增强社区安全铺平了道路。为此，该项目制定了社会技术进步方案，以有效分析和协助社区和管理机构做出实时、明智的反应。该项目支持摄像头（边缘节点）附近的实时视觉处理和边缘网络上的协同处理。同时，拟议的研究解释、形式化和建模公共安全和安全事件，使其能够被机器检测到，减少偏见，并实现广泛的社区支持和信任。通过展示强大的新兴边缘计算技术的使用，该项目将突出这些技术的适用性和适应性，以应对许多社区挑战和更广泛的智慧城市和网络物理系统（CPS）应用，包括智能交通和行人安全。此外，拟议的以社区为基础的试点将成为全国其他社区的榜样。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。

The emergence of intelligent technologies is enabling a new era of connection between community residents and the surrounding environments, both in the United States and around the world. With the new wave of growth in urban areas, ensuring public safety is an essential precursor toward "smart" cities and communities. This project proposes a novel "intelligent" policing technology as a transformative solution to efficiently enhance law enforcement, while minimizing unnecessary interactions and maintaining resident privacy. The proposed technology offers a network of smart cameras that do not require continuous monitoring, but instead are trained to generate alerts on the spot in real-time. Since the cameras identify behaviors and not identities, they can reduce biases, minimize false alarms, and protect personal privacy. The intelligent policing technology will be co-designed and co-created with the direct help of community residents, neighborhood leaders, and local business owners, as well as agencies including the City of Charlotte, and local law enforcement agencies in Charlotte-Mecklenburg and Gaston counties. The proposed research makes fundamental advances in multiple areas from computer vision, computer architecture, and real-time edge computing, as well as criminology and community-technology interaction. It paves the path for bringing the recent advances in deep learning and data analytics to enhance the safety and security of communities without jeopardizing the privacy of residents. To this end, this project formulates social-technical advances to efficiently analyze and assist communities and governing agencies in making real-time, smart reactions. The project enables real-time vision processing near the cameras (edge nodes) and cooperative processing over the edge network. At the same time, the proposed research interprets, formalizes, and models public safety and security events to be machine detectable, reducing biases, and enabling broad-based community support and trust. By demonstrating the use of powerful emerging edge computing technologies, the project will highlight the applicability and adaptability of such technologies to tackle many community challenges and broader smart cities and cyber-physical systems (CPS) applications, including smart transportation and pedestrian safety. Additionally, the proposed community-based pilots will serve as exemplars to other communities across the nation.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.