Optimized Multi-Shaped Traffic Light Detection and Signal Recognition with Guided Framework

2018 ◽  
Author(s):  
R.P.U.N. Rajapaksha ◽  
L. Ranathunga
Keyword(s):  
Signal Recognition ◽  
Traffic Light ◽  
Light Detection

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.

HSI COLOR CONVERSION AND HOG FEATURE EXTRACTION IN TRAFFIC LIGHT DETECTION USING AdaBSF

2017 ◽  
Vol 5 (3) ◽  
pp. 20
Author(s):  
JEBISHA J ◽  
MONISHA V ◽  
JEMI B. FEMILA ◽  
◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Traffic Light ◽  
Light Detection ◽  
Color Conversion

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.

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