Real-Time Edge Intelligence for Smart Communities

Project

Real-time Edge Intelligence for Smart Communities

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.

Student

Christopher Neff

A Combat Veteran of the United States Army, Chris is currently a Graduate Research Assistant pursuing a PhD in Electrical Engineering at the University of North Carolina at Charlotte. Chris received a Bachelor’s in Computer Engineering from the University of North Carolina at Charlotte, graduating Magna Cum Laude and receiving the award for Most Outstanding Senior in Computer Engineering in May 2019. During his undergrad, Chris participated in the Co-op program, working as a LifeCycle Engineer at Robert E. Mason Company. He continued to work with R. E. Mason part time during his Junior and Senior years, gaining a foundation of knowledge and experience in the Industrial Automation industry. For his Senior Design project, Chris worked with Industry experts from Nvidia and graduate student from UNC Charlotte to design and implement a multi-camera pedestrian tracking system built upon the Nvidia Jeston Xavier embedded GPU. The project was successful, becoming a finalist at the 2019 Engineering Senior Design Expo and leading to publications in IEEE SoutheastCon and a live demonstration of the technology at the 2019 Smart Cities Connect Conference and Expo in Denver Colorado. This project is what sparked Chris’ interest in graduate study and lead into his current area of research. Under the mentorship of Dr. Hamed Tabkhi, Chris is a member of the Transformative Computer Systems and Architecture Research lab and leads a thirteen-member team in the Real-time Edge Intelligence for Smart Communities project. He is also involved with community outreach, volunteer youth AI education, working with Central Piedmont Community College students on ethical data creation, and partial mentorship of a Senior Design team.