scholarly journals Context-aware GPS integrity monitoring for intelligent transport systems

2016 ◽  
Vol 3 (1) ◽  
pp. 1-15 ◽  
Author(s):  
T.A. Binjammaz ◽  
Ali H. Al-Bayatti ◽  
A.H. Al-Hargan
Keyword(s):  
Transport Systems ◽  
Context Aware ◽  
Intelligent Transport Systems ◽  
Integrity Monitoring ◽  
Intelligent Transport

Transport Oriented Framework for Context-Aware Services Management

2021 ◽  
Vol 110 ◽  
pp. 63-69
Author(s):  
Celestin Drăgănescu
Keyword(s):  
Software Architecture ◽  
Contextual Information ◽  
Transport Systems ◽  
Context Aware ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Architecture Model ◽  
Vehicle Communication ◽  
Adaptive Framework

The paper proposes a dynamic adaptive framework that responds to the needs of context-aware services provided by the intelligent transport systems. The executive core of this framework is a software architecture model that ensures the running of an application which is adaptable to context changes. The solution was tested on a case study that highlights provided contextual information through an inter-vehicle communication system.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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