A REAL TIME TRAFFIC LIGHT RECOGNITION SYSTEM

2008 ◽  
Vol 05 (02) ◽  
pp. 149-161 ◽  
Author(s):  
XIAOLI YANG ◽  
ZHENPENG ZHAO ◽  
YOUN K. KIM
Keyword(s):  
Real Time ◽  
Shape Analysis ◽  
Feature Matching ◽  
Median Filter ◽  
Recognition System ◽  
Transport Systems ◽  
Traffic Light ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Color Detection

Driving is a challenging task, especially for those with color vision deficiencies. Intelligent Transport Systems (ITS) can provide useful information and make driving safe and convenient. A real time traffic light recognition system is presented in this paper for improving public safety and facilitating color deficient drivers. It may also be included as a part of ITS. The presented system consists of a digital video camera to record traffic lights and a portable PC to process images in real time. It uses various techniques of color detection, feature matching with normalized cross-correlation (NCC), and mathematical shape analysis for the traffic light recognition. For color detection, we obtained an initial solution by using RGB component adjustment, thresholding algorithm, and median filter. In dealing with illumination changes with weather and time, a simple adaptation method was developed. Feature matching with NCC was used after color detection to further detect and recognize the traffic lights. To improve the system's tolerance and robustness, a mathematical shape analysis was undertaken to obtain the final results. Numerous experiments were conducted to demonstrate the effectiveness and practicability of the system with images under different weather conditions. The average recognition ratio is higher than 95% from the testing results. The average processing time is 30 ms per frame, making the system suitable in real time conditions. Audio alert is added to the current system as an integral part of a portable system to be developed.

scholarly journals Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

2020 ◽  
Author(s):  
Hendrik Macedo ◽  
Thiago Almeida ◽  
Leonardo Matos ◽  
Bruno Prado
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Background Suppression ◽  
Processing Unit ◽  
Traffic Light ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Computational Resources

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

scholarly journals Real Time Traffic Light Detection by Autonomous Vehicles using Artificial Neural Network Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 2129-2133
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Traffic Light ◽  
Artificial Intelligence Technique ◽  
Light Detection ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
The Right ◽  
Sufficient Precision

Autonomous vehicles are the reality of the future, they will open up the way for future advanced systems where computers are expected to take over the decision making of driving. These automobiles are capable of sensing their environment and moving with little or no human input. The main goal of this research is to detect traffic light in real-time for autonomous vehicles. Apart from taking decisions to navigate in the right manner the autonomous vehicles important task is to detect traffic lights, so that it can obey the traffic rules with sufficient precision. The work carried out in this research makes use of two Artificial Intelligence technique, these techniques are compared in accomplishing the task of traffic light detection in real time. The two models that are designed and implemented are Convolution neural network (CNN) and Deep Convolution Inverse Graphics Network (DCIGN). The results clearly show that DCIGN out performance CNN by 8%.

scholarly journals Real-Time Traffic Light Detection and Interpretation using Circle Centroid

2019 ◽  
Vol 8 (12S2) ◽  
pp. 465-469
Keyword(s):  
Real Time ◽  
Hough Transform ◽  
Weather Condition ◽  
Traffic Light ◽  
Image Processing Algorithm ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Yellow Color ◽  
Red Color ◽  
Different Color

The paper proposes a new method to recognize the sequence of a traffic light using image processing algorithm. Invariant in factor lightning and weather condition that lead to misinterpret the color of traffic light is one of the factors of accident at traffic light conjunction besides the behavior of the driver itself. The process to identify the color and shape of traffic light are Image Acquisition, Pre-Processing, Detection, Feature Extraction and Interpretation. RGB normalization is performed and simple thresholding method that acts as color segmentation provides a better division of the traffic light colors. Circle Hough Transform and HSV color features based on the traffic light aspect are used to decide whether the spots on the frames are likely to be traffic lights’ color and shape. The detection of traffic light will be obtained after identifying the feature such as the centroid of the Circle Hough Transform that need to be extracted at the end of the result. The research has been improved by focusing on detection and interpretation of traffic light based on real time video rather than image sample as the input. The proposed algorithm can detect the green color accurately with maximum accuracy of 83.8%, yellow color of 75.6% and red color with minimum accuracy of 70.19%. This indicates that there is a possibility to use the proposed algorithm to detect the three different color of green, yellow and red color of traffic lights’ colour.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

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