Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81186
Title: A computer vision-based roadside occupation surveillance system for intelligent transport in smart cities
Authors: Ho, GTS
Tsang, YP 
Wu, CH
Wong, WH
Choy, KL 
Keywords: Computer vision
Roadside occupation
Smart city
Smart mobility
Traffic surveillance
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Sensors (Switzerland), 2019, v. 19, no. 8 How to cite?
Journal: Sensors (Switzerland) 
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.
URI: http://hdl.handle.net/10397/81186
ISSN: 1424-8220
DOI: 10.3390/s19081796
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Ho GTS, Tsang YP, Wu CH, Wong WH, Choy KL. A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities. Sensors. 2019; 19(8):1796, is available at https://doi.org/10.3390/s19081796
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