Road Traffic Optimization for Mid-sized African Cities – Application of Fuzzy Algorithms and Computer Vision

2019 ◽  
Author(s):  
Sechocha Liphoto ◽  
Muthoni Masinde
Keyword(s):  
Computer Vision ◽  
Road Traffic ◽  
African Cities ◽  
Fuzzy Algorithms ◽  
Traffic Optimization

scholarly journals An autonomous navigational system using GPS and computer vision for futuristic road traffic

2022 ◽  
Vol 12 (1) ◽  
pp. 179
Author(s):  
Prabha Ramasamy ◽  
Mohan Kabadi
Keyword(s):  
Computer Vision ◽  
Global Positioning System ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Road Traffic ◽  
Positioning System ◽  
Traffic System ◽  
Global Positioning ◽  
Navigational System

Navigational service is one of the most essential dependency towards any transport system and at present, there are various revolutionary approaches that has contributed towards its improvement. This paper has reviewed the global positioning system (GPS) and computer vision based navigational system and found that there is a large gap between the actual demand of navigation and what currently exists. Therefore, the proposed study discusses about a novel framework of an autonomous navigation system that uses GPS as well as computer vision considering the case study of futuristic road traffic system. An analytical model is built up where the geo-referenced data from GPS is integrated with the signals captured from the visual sensors are considered to implement this concept. The simulated outcome of the study shows that proposed study offers enhanced accuracy as well as faster processing in contrast to existing approaches.

scholarly journals Road Traffic Analysis Using Computer Vision

2021 ◽  
Vol 9 (8) ◽  
pp. 2002-2006
Author(s):  
Needhi U. Gaonkar
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Traffic Analysis ◽  
Transportation Systems ◽  
Visual Object ◽  
Highway Traffic ◽  
Analysis System

Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.

Computer Vision in Control and Optimization of Road Traffic

2020 ◽  
Author(s):  
Vladyslav Zinchenko ◽  
Galyna Kondratenko ◽  
Ievgen Sidenko ◽  
Yuriy Kondratenko
Keyword(s):  
Computer Vision ◽  
Road Traffic

scholarly journals Utilization of the methods of artificial intelligence for organization of unmanned traffic

2020 ◽  
pp. 51-58
Author(s):  
Roman Dushkin ◽  
Mikhail Grigor'evich Andronov
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Traffic Management ◽  
Road Traffic ◽  
Multi Agent Systems ◽  
Special Contribution ◽  
Agent Systems ◽  
Multi Agent ◽  
Computer Vision Systems ◽  
Central Control System

This article meticulously examines the questions of application of certain technologies of multi-agent systems theory in the area of unmanned traffic management for combatting the so-called “generative adversarial attacks” on the computer vision systems that are used in such vehicles. The article provides examples of generative-adversarial attacks on various types of neural networks, as well as describes the problems that arise when using computer vision. Possible solutions to these problems are proposed. Research methodology includes the theory of multi-agent systems applicable to automobile transport, which suggests using the so-called V2X-interaction, i.e. constant exchange of information between the vehicle and various actors involved in road traffic – a central control system, other vehicles, roadside infrastructure and pedestrians. The authors’ special contribution to this research lies in application of the theory of multi-agent systems for traffic arrangement with consideration of its actors as the agents with diverse roles. The novelty consists in employment of one of the methods of artificial intelligence in solution of the problems, obtained due to the use of other methods of artificial intelligence (recognition of images in computer vision). The relevance of the study is based on the detailed coverage of the questions of organization of unmanned traffic on training grounds and public roads.

scholarly journals Determination of the Dynamic Vehicle Model Parameters by Means of Computer Vision

2019 ◽  
Vol 21 (3) ◽  
pp. 28-34
Author(s):  
Daniil A. Loktev ◽  
Alexey A. Loktev ◽  
Alexandra V. Salnikova ◽  
Anna A. Shaforostova
Keyword(s):  
Computer Vision ◽  
Road Traffic ◽  
Deformable Body ◽  
Model Parameters ◽  
Vehicle Model ◽  
The Road ◽  
Complex Approach ◽  
On The Road ◽  
The Individual

This study is devoted to determining the geometric, kinematic and dynamic characteristics of a vehicle. To this purpose, it is proposed to use a complex approach applying the models of deformable body mechanics for describing the oscillatory movements of a vehicle and the computer vision algorithms for processing a series of object images to determine the state parameters of a vehicle on the road. The model of the vehicle vertical oscillations is produced by means of the viscoelastic elements and the dry friction element that fully enough represent the behavior of the sprung masses. The introduced algorithms and models can be used as a part of a complex system for monitoring and controlling the road traffic. In addition, they can determine both the speed of the car and its dynamic parameters and the driving behavior of the individual drivers.

Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling

2018 ◽  
Vol 22 (5) ◽  
pp. 704-710 ◽  
Author(s):  
Robert Kerwin C. Billones ◽  
◽  
Argel A. Bandala ◽  
Laurence A. Gan Lim ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Volume Estimation ◽  
Public Utility ◽  
Scene Analysis ◽  
Volume Distribution ◽  
Distribution Analysis ◽  
Traffic Scene Analysis

This paper presents the development of a vision-based system for microscopic road traffic scene analysis and understanding using computer vision and computational intelligence techniques. The traffic flow model is calibrated using the information obtained from the road-side cameras. It aims to demonstrate an understanding of different levels of traffic scene analysis from simple detection, tracking, and classification of traffic agents to a higher level of vehicular and pedestrian dynamics, traffic congestion build-up, and multi-agent interactions. The study used a video dataset suitable for analysis of a T-intersection. Vehicle detection and tracking have 88.84% accuracy and 88.20% precision. The system can classify private cars, public utility vehicles, buses, and motorcycles. Vehicular flow of every detected vehicles from origin to destination are also monitored for traffic volume estimation, and volume distribution analysis. Lastly, a microscopic traffic model for a T-intersection was developed to simulate a traffic response based on actual road scenarios.

A Segmentation Method of Traffic Marking Based on Average Gray Approximating the Optimal Threshold

2014 ◽  
Vol 556-562 ◽  
pp. 3510-3513 ◽  
Author(s):  
Zu Sheng Chen ◽  
You Fu Wu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Crucial Role ◽  
Road Traffic ◽  
Image Database ◽  
Identification Problem ◽  
Optimal Threshold ◽  
Segmentation Method ◽  
Local Average

Image segmentation technique was used widely for computer vision and image processing. A robust technique of image segmentation plays a crucial role in identification problem. In this paper, a nonparametric and unsupervised method of automatic threshold for segmenting image was proposed, i.e. the optimal threshold is approximated by global average gray and local average gray, and this method was compared with other methods by using standard image. The experimental results show that our method proposed in this paper is robust. In addition, an image database of road traffic marking (www.ananth.in/RoadMarkingdetection.html) is provided to do this experiment for testing our method, the results show that our method is excellent.

A PARALLEL PROCESSING MODEL FOR REAL-TIME COMPUTER VISION-AIDED ROAD TRAFFIC MONITORING

1992 ◽  
Vol 02 (02n03) ◽  
pp. 257-264 ◽  
Author(s):  
A. T. ALI ◽  
E. L. DAGLESS
Keyword(s):  
Computer Vision ◽  
Parallel Processing ◽  
Real Time ◽  
Road Traffic ◽  
Data Extraction ◽  
Image Data ◽  
Traffic Monitoring ◽  
Traffic Data ◽  
Video Images ◽  
Parallel Processing Model

A transputer-based parallel processing paradjgm for real-time extraction of road traffic data from video images of roadway scenes is proposed. The model can monitor three lanes of motorway traffic in real-time by processing images from two windows associated with each lane. Parallel algorithms are distributed among a network of transputers to perform similar and/or different tasks concerning image data analysis and traffic data extraction. The model can be expanded to cover more lanes or duplicated to monitor a further multi-lane carriageway.

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