scholarly journals Traffic Light Switching by Traffic Density Measurement using Image Processing Technique

IJIREEICE ◽  
2017 ◽  
Vol 5 (5) ◽  
pp. 319-324
Author(s):  
Mrs. Harshitha R ◽  
Chandan R ◽  
Poornima K ◽  
Navyashree U N ◽  
Sandesh Gowda P
Keyword(s):  
Image Processing ◽  
Density Measurement ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Traffic Density ◽  
Traffic Light

An Advanced Fuzzy Logic Based Traffic Controller

2014 ◽  
Vol 5 (1) ◽  
pp. 31-40
Author(s):  
Bilal Ahmed Khan ◽  
Nai Shyan Lai
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Traffic Management ◽  
Fuzzy Logic Controller ◽  
Current Model ◽  
Traffic Signals ◽  
Processing Technique ◽  
Urban Traffic ◽  
Image Processing Technique ◽  
Traffic Light

Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisition, the process is further linked to the fuzzy logic controller which generates a unique output for each input pattern. Here image processing and fuzzy logic tool boxes of MATLAB are used where the final output is sent to Peripheral Interface Controller (PIC) microcontroller to drive the traffic signals in the desired manner. The results obtained show an improvement of 44% in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model.

An Advanced Fuzzy Logic Based Traffic Controller

2015 ◽  
pp. 1490-1499
Author(s):  
Bilal Ahmed Khan ◽  
Nai Shyan Lai
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Traffic Management ◽  
Fuzzy Logic Controller ◽  
Current Model ◽  
Traffic Signals ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Traffic Light ◽  
Logic Control

Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisition, the process is further linked to the fuzzy logic controller which generates a unique output for each input pattern. Here image processing and fuzzy logic tool boxes of MATLAB are used where the final output is sent to Peripheral Interface Controller (PIC) microcontroller to drive the traffic signals in the desired manner. The results obtained show an improvement of 44% in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model.

scholarly journals Detection of Traffic Light using Machine Vision for Autonomous Vehicles Application

2019 ◽  
Vol 9 (2) ◽  
pp. 1033-1037
Keyword(s):  
Image Processing ◽  
Urban Areas ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Traffic Light ◽  
Light Detection ◽  
Traffic Lights

Traffic light detection is crucial to decrease the traffic light accidents at intersections and to realize autonomous driving. There are so many existing methods to detect traffic light. However, these approaches have several limitations, such as not function well in complex driving environments. Hence, to overcome such constraints, the traffic light detection for the autonomous vehicle using image processing technique is proposed. The experiments are carried out using 114 scene images that consist of 209 traffic lights with different angles, weather conditions, and distance. An image processing technique, Hough Circle Transform is used in this traffic light detection system with the help of Gaussian blurring and Sobel filter. So, the overall accuracy rate for the proposed algorithm is 75.59%. This system is possible to be used in urban areas or complex environments, whether it is at night or day, and it able to detect the traffic light regardless of the colour changes.

scholarly journals Application of traffic conflict technique for traffic safety evaluation at intersection based on image processing

2021 ◽  
Vol 7 (3A) ◽  
pp. 134-142
Author(s):  
Ngoc Dung Bui ◽  
Dinh Tran Ngoc Huy ◽  
Tuan Thanh Nguyen
Keyword(s):  
Image Processing ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Economic Damage ◽  
Traffic Light ◽  
Safety Level

Traffic accidents occur frequently at intersections area because of conflicts among vehicles. Many researchers have been assessing traffic safety, however most of them are based on accident data that happened and caused injury as well as economic damage. Recently, conflict techniques provide general views and solutions to prevent early collisions. This paper proposes a method of conflict analysis using image processing technique and fuzzy comprehensive evaluation. Based on vehicles detection, the conflict parameters collected from two intersections in Hanoi, Vietnam were processed and evaluated by fuzzy comprehensive evaluation to give out the safety level of conflict points. The experimental results show that the proposed methods have successfully shown the distribution and location of dangerous conflict points according to the actual situation of the two intersections. Based on our results, authorities can consider reorganizing traffic light pattern at these intersections to reduce possible collisions.

Design of Lighting Intelligent Control System Based on OpenCV Image Processing Technology

2021 ◽  
Vol 29 (Supp01) ◽  
pp. 119-139
Author(s):  
Suning Gong ◽  
Rakesh Kumar ◽  
D. Kumutha
Keyword(s):  
Image Processing ◽  
Control System ◽  
Traffic Flow ◽  
Public Transportation ◽  
Traffic Control ◽  
Urban Areas ◽  
Light Signal ◽  
Image Processing Technique ◽  
Traffic Density ◽  
Traffic Light

The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic.

A New Image Processing Technique for STEM

1974 ◽  
Vol 32 ◽  
pp. 432-433
Author(s):  
Yasushi Kokubo ◽  
Hirotami Koike ◽  
Teruo Someya
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Elastic Scattering ◽  
Field Emission ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Energy Analyzer ◽  
Scanning Microscope ◽  
Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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