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A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities
George To Sum Ho1, Yung Po Tsang2, Chun Ho Wu3
1Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China. georgeho@hsu.edu.hk.
Sensors (Basel, Switzerland)
|April 18, 2019
Summary
A new computer vision-based roadside occupation surveillance system (CVROSS) uses smart cameras to monitor loading bays in real-time. This system aids smart mobility by improving traffic flow and roadside activity transparency.
Area of Science:
- Computer Vision and IoT for Smart Cities
- Intelligent Transportation Systems (ITS)
- Traffic Management and Urban Planning
Background:
- Smart mobility is crucial for digital and green city initiatives, facing challenges from double-parking and commercial vehicle activities in dense urban areas.
- Existing traffic surveillance systems often lack real-time capabilities for monitoring specific roadside activities like loading and unloading.
- High transportation density exacerbates traffic congestion due to inefficient roadside space utilization.
Purpose of the Study:
- To develop and validate a real-time Internet of Things (IoT)-based system for monitoring roadside loading and unloading bays.
- To enhance smart city infrastructure by providing real-time traffic surveillance and decision support for roadside occupancy.
- To improve traffic and fleet management through enhanced transparency of roadside activities.
Main Methods:
- Implementation of a computer vision-based roadside occupation surveillance system (CVROSS) using high-definition smart cameras and wireless communication.
- Automatic capture of real-time roadside traffic images, specifically focusing on loading/unloading activities, via a vision-based network.
- Application of fuzzy logic for evaluating roadside occupancy/vacancy status and data visualization for user transparency.
Main Results:
- Successful design and testing of the CVROSS system in Hong Kong.
- Validation of the system's accuracy in parking-gap estimation and overall performance.
- Demonstrated potential for real-time decision support regarding roadside bay utilization.
Conclusions:
- The CVROSS system offers an effective, integrated solution for real-time surveillance of roadside loading bays.
- The system contributes to smart mobility by providing valuable data for traffic and fleet management.
- Fuzzy logic integration enhances the decision-making process and user understanding of roadside dynamics.
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