ArticleSensors (Basel, Switzerland)2019
A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Underwater image enhancement based on optimally weighted histogram framework and improved Fick's law algorithm.Scientific reports · 2024Article
- Sensors on Internet of Things Systems for the Sustainable Development of Smart Cities: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2024Review
- Application of Wireless Magnetic Sensors in the Urban Environment and Their Accuracy Verification.Sensors (Basel, Switzerland) · 2023Article
- Drivers' Subjective Assessment of the Ease of Finding a Vacant Parking Space in an Area Equipped with Vehicle Detection Devices.Sensors (Basel, Switzerland) · 2022Article
- Vision-Based Pedestrian's Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System.Sensors (Basel, Switzerland) · 2022Article
- An Intelligent Traffic Surveillance System Using Integrated Wireless Sensor Network and Improved Phase Timing Optimization.Sensors (Basel, Switzerland) · 2022Article
- Article
- Tracking droplets in soft granular flows with deep learning techniques.European physical journal plus · 2021Article
- Vehicle Spatial Distribution and 3D Trajectory Extraction Algorithm in a Cross-Camera Traffic Scene.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.