Traffic light detection and recognition for autonomous vehicles

2015 ◽  
Vol 22 (1) ◽  
pp. 50-56 ◽  
Author(s):  
Guo Mu ◽  
Zhang Xinyu ◽  
Li Deyi ◽  
Zhang Tianlei ◽  
An Lifeng
Keyword(s):  
Autonomous Vehicles ◽  
Traffic Light ◽  
Light Detection ◽  
Detection And Recognition

scholarly journals Prototyping a Traffic Light Recognition Device with Expert Knowledge

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 278 ◽  
Author(s):  
Thiago Almeida ◽  
Hendrik Macedo ◽  
Leonardo Matos ◽  
Nathanael Vasconcelos
Keyword(s):  
Expert Knowledge ◽  
Support Vector ◽  
Background Suppression ◽  
Processing Unit ◽  
Driver Assistance ◽  
Traffic Light ◽  
Accuracy Rate ◽  
Light Detection ◽  
Vector Machines ◽  
Detection And Recognition

Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. ML algorithms require a large amount of data to work properly and, thus, a lot of computational power is required to analyze the data. We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used to reduce the amount of data and improve the accuracy rate for traffic light detection and recognition. Results show an improvement in the accuracy rate around 15%. The paper also proposes a TLR device prototype using both camera and processing unit of a smartphone which can be used as a driver assistance. To validate such layout prototype, a dataset was built and used to test an ML model based on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs). Results show 100% precision rate and recall of 65%.

scholarly journals TRAFFIC LIGHT DETECTION USING CONIC SECTION GEOMETRY

2016 ◽  
Vol III-1 ◽  
pp. 191-200
Author(s):  
S. Hosseinyalmdary ◽  
A. Yilmaz
Keyword(s):  
Prior Knowledge ◽  
Pose Estimation ◽  
Open Problem ◽  
Autonomous Vehicles ◽  
Conic Section ◽  
Traffic Light ◽  
Light Detection ◽  
State Recognition ◽  
Traffic Lights ◽  
Stereo Cameras

Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.

Traffic light detection and recognition based on Haar-like features

2018 ◽  
Author(s):  
Sang-Hyuk Lee ◽  
Jung-Hawn Kim ◽  
Yong-Jin Lim ◽  
Joonhong Lim
Keyword(s):  
Traffic Light ◽  
Light Detection ◽  
Detection And Recognition

scholarly journals TRAFFIC LIGHT DETECTION USING CONIC SECTION GEOMETRY

2016 ◽  
Vol III-1 ◽  
pp. 191-200 ◽  
Author(s):  
S. Hosseinyalmdary ◽  
A. Yilmaz
Keyword(s):  
Prior Knowledge ◽  
Pose Estimation ◽  
Open Problem ◽  
Autonomous Vehicles ◽  
Conic Section ◽  
Traffic Light ◽  
Light Detection ◽  
State Recognition ◽  
Traffic Lights ◽  
Stereo Cameras

Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.

scholarly journals Traffic Light and Arrow Signal Recognition Based on a Unified Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8066
Author(s):  
Tien-Wen Yeh ◽  
Huei-Yung Lin ◽  
Chin-Chen Chang
Keyword(s):  
High Performance ◽  
Focal Length ◽  
Computation Time ◽  
Urban Traffic ◽  
Signal Recognition ◽  
Traffic Light ◽  
Light Detection ◽  
Traffic Lights ◽  
Map Data ◽  
Detection And Recognition

We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length cameras for traffic light detection at various distances. For recognition, we propose a new algorithm that combines object detection and classification to recognize the light state classes of traffic lights. Furthermore, we use a unified network by sharing features to decrease computation time. The results reveal that the proposed approach enables high-performance traffic light detection and recognition.

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 An Efficient Color Space for Deep-Learning Based Traffic Light Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hyun-Koo Kim ◽  
Ju H. Park ◽  
Ho-Youl Jung
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Detection System ◽  
Color Space ◽  
Network Models ◽  
Recognition System ◽  
Color Spaces ◽  
Traffic Light ◽  
Light Detection

Traffic light recognition is an essential task for an advanced driving assistance system (ADAS) as well as for autonomous vehicles. Recently, deep-learning has become increasingly popular in vision-based object recognition owing to its high performance of classification. In this study, we investigate how to design a deep-learning based high-performance traffic light detection system. Two main components of the recognition system are investigated: the color space of the input video and the network model of deep learning. We apply six color spaces (RGB, normalized RGB, Ruta’s RYG, YCbCr, HSV, and CIE Lab) and three types of network models (based on the Faster R-CNN and R-FCN models). All combinations of color spaces and network models are implemented and tested on a traffic light dataset with 1280×720 resolution. Our simulations show that the best performance is achieved with the combination of RGB color space and Faster R-CNN model. These results can provide a comprehensive guideline for designing a traffic light detection system.

Sign in / Sign up

Export Citation Format

Share Document