1 research outputs found
A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices
Cloud computing has been a main-stream computing service for years. Recently,
with the rapid development in urbanization, massive video surveillance data are
produced at an unprecedented speed. A traditional solution to deal with the big
data would require a large amount of computing and storage resources. With the
advances in Internet of things (IoT), artificial intelligence, and
communication technologies, edge computing offers a new solution to the problem
by processing the data partially or wholly on the edge of a surveillance
system. In this study, we investigate the feasibility of using edge computing
for smart parking surveillance tasks, which is a key component of Smart City.
The system processing pipeline is carefully designed with the consideration of
flexibility, online surveillance, data transmission, detection accuracy, and
system reliability. It enables artificial intelligence at the edge by
implementing an enhanced single shot multibox detector (SSD). A few more
algorithms are developed on both the edge and the server targeting optimal
system efficiency and accuracy. Thorough field tests were conducted in the
Angle Lake parking garage for three months. The experimental results are
promising that the final detection method achieves over 95% accuracy in
real-world scenarios with high efficiency and reliability. The proposed smart
parking surveillance system can be a solid foundation for future applications
of intelligent transportation systems