A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices
Ruimin Ke, Yifan Zhuang, Ziyuan Pu, Yinhai Wang

TL;DR
This paper presents a novel edge computing-based smart parking surveillance system that leverages AI for real-time detection, achieving over 95% accuracy with high efficiency and reliability in real-world tests.
Contribution
It introduces an edge AI system with an enhanced SSD detector for smart parking, optimizing accuracy and efficiency in urban surveillance.
Findings
Over 95% detection accuracy in real-world parking scenarios
High system efficiency and reliability demonstrated in field tests
Effective edge-based AI processing for smart city applications
Abstract
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…
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