期刊
SENSORS
卷 19, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/s19081796
关键词
smart mobility; computer vision; roadside occupation; traffic surveillance; smart city
资金
- Research Office, The Hong Kong Polytechnic University (PolyU)
- Department of Industrial and Systems Engineering (ISE), The Hong Kong Polytechnic University (PolyU)
- Department of Supply Chain and Information Management (SCM), The Hang Seng University of Hong Kong (HSUHK)
- Big Data and Artificial Intelligence Group of HSUHK
- ISE of PolyU
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.
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