SCC: Building Safe and Secure Communities through Real-Time Edge Video Analytics
SCC:通过实时边缘视频分析构建安全可靠的社区
基本信息
- 批准号:1831795
- 负责人:
- 金额:$ 189.75万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-10-01 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
无论是在美国还是在世界各地,智能技术的出现正在推动社区居民与周围环境之间建立联系的新时代。随着城市地区的新一轮增长,确保公共安全是实现“智能”城市和社区的重要前提。该项目提出了一种新型的“智能”警务技术,作为有效加强执法的变革性解决方案,同时最大限度地减少不必要的互动并维护居民隐私。这项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,实时生成警报。由于摄像头识别行为而不是身份,因此可以减少偏见,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,社区领导人和当地企业主以及包括夏洛特市在内的机构以及夏洛特-梅克伦堡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建。拟议的研究在计算机视觉、计算机架构和实时边缘计算以及犯罪学和社区技术互动等多个领域取得了根本性进展。它为深度学习和数据分析的最新进展铺平了道路,以提高社区的安全性,而不会危及居民的隐私。为此,该项目制定了社会技术进步,以有效地分析和协助社区和管理机构做出实时,智能的反应。该项目实现了摄像机(边缘节点)附近的实时视觉处理和边缘网络上的协作处理。与此同时,拟议的研究将公共安全和安全事件解释、形式化和建模为机器可检测的,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,拟议中的社区试点项目将成为全国其他社区的典范。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
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge
- DOI:10.1109/access.2021.3055775
- 发表时间:2021-01-01
- 期刊:
- 影响因子:3.9
- 作者:George, Anjus;Ravindran, Arun;Tabkhi, Hamed
- 通讯作者:Tabkhi, Hamed
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Hamed Tabkhi其他文献
Design Study on Impact of Memory Access Parallelism for Cloud FPGAs
存储器访问并行性对云 FPGA 影响的设计研究
- DOI:
10.1109/socc52499.2021.9739477 - 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Arnab A. Purkayastha;Hamed Tabkhi - 通讯作者:
Hamed Tabkhi
BSMO: enhancing multitask learning through batch swapping optimization
BSMO:通过批量交换优化增强多任务学习
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Zephaniah Spencer;G. Schirner;Hamed Tabkhi - 通讯作者:
Hamed Tabkhi
An Asymmetric Checkpointing and Rollback Error Recovery Scheme for Embedded Processors
嵌入式处理器的非对称检查点和回滚错误恢复方案
- DOI:
10.1109/dft.2008.27 - 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
Hamed Tabkhi;S. Miremadi;A. Ejlali - 通讯作者:
A. Ejlali
Taming the Memory Demand Complexity of Adaptive Vision Algorithms
降低自适应视觉算法的内存需求复杂性
- DOI:
10.1007/978-3-319-90023-0_12 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Majid Sabbagh;Hamed Tabkhi;G. Schirner - 通讯作者:
G. Schirner
Power-efficient real-time solution for adaptive vision algorithms
适用于自适应视觉算法的高能效实时解决方案
- DOI:
10.1049/iet-cdt.2014.0075 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Hamed Tabkhi;Majid Sabbagh;G. Schirner - 通讯作者:
G. Schirner
Hamed Tabkhi的其他文献
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{{ truncateString('Hamed Tabkhi', 18)}}的其他基金
I-Corps: Privacy-Responsive Artificial Intelligence-Based Solution for Smart Video Surveillance
I-Corps:基于隐私敏感的人工智能的智能视频监控解决方案
- 批准号:
2323757 - 财政年份:2023
- 资助金额:
$ 189.75万 - 项目类别:
Standard Grant
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 - 财政年份:2023
- 资助金额:
$ 189.75万 - 项目类别:
Continuing Grant
CPS: Small: Worker-in-the-loop real time safety system for short-duration highway workzones
CPS:小型:适用于短期高速公路工作区的工人循环实时安全系统
- 批准号:
1932524 - 财政年份:2019
- 资助金额:
$ 189.75万 - 项目类别:
Standard Grant
SCC-Planning: Pedestrian Safe and Secure Communities with Ambient Machine Vision
SCC-Planning:利用环境机器视觉打造安全可靠的行人社区
- 批准号:
1737586 - 财政年份:2017
- 资助金额:
$ 189.75万 - 项目类别:
Standard Grant
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