A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities

TLDR

Smart mobility is central to digital and green city initiatives, yet double‑parking and frequent truck loading/unloading disrupt traffic in dense urban areas, prompting the development of the CVROSS system. The study aims to develop a real‑time IoT‑based system to monitor roadside loading and unloading bays. High‑definition smart cameras with wireless links capture real‑time roadside images, which are analyzed via fuzzy logic to assess occupancy and vacancy and visualized for users, with the system tested in Hong Kong to validate parking‑gap estimation and performance. Testing in Hong Kong demonstrated accurate parking‑gap estimation and reliable system performance, supporting traffic and fleet management in smart mobility.

Abstract

In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility.