scholarly journals Traffic signal light detection and recognition based on canny operator

2021 ◽  
Author(s):  
Guo Shuqing ◽  
Liu Yuming
Keyword(s):  
Traffic Signal ◽  
Light Detection ◽  
Canny Operator ◽  
Signal Light ◽  
Detection And Recognition

Traffic Signal Light Detection in Electronic Travel Aid System

2013 ◽  
Vol 12 (14) ◽  
pp. 2768-2773
Author(s):  
Xu Jie ◽  
Fang Zhi-Gang
Keyword(s):  
Traffic Signal ◽  
Light Detection ◽  
Signal Light

Traffic signal light detection through sunglare filters of different Q factors

1997 ◽  
Vol 22 (1) ◽  
pp. 24-31 ◽  
Author(s):  
David A. Palmer ◽  
John Mellerio ◽  
Amanda Cutler
Keyword(s):  
Traffic Signal ◽  
Light Detection ◽  
Signal Light ◽  
Q Factors

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%.

LED traffic signal light which considered people of a low vision

2005 ◽  
Vol 3 (4) ◽  
Author(s):  
M. Kaburaki ◽  
Y. Nakashima ◽  
M. Takamatsu ◽  
K. Mima ◽  
S. Nakajima
Keyword(s):  
Low Vision ◽  
Traffic Signal ◽  
Signal Light

Specification of Road Traffic Signal Light Intensity

1968 ◽  
Vol 10 (3) ◽  
pp. 245-254 ◽  
Author(s):  
Barry L. Cole ◽  
Brian Brown
Keyword(s):  
Experimental Data ◽  
Light Intensity ◽  
Signal Intensity ◽  
Road Traffic ◽  
Spatial Summation ◽  
Traffic Signal ◽  
Background Luminance ◽  
Signal Light ◽  
Threshold Criterion ◽  
Reported Data

In a previous communication we reported data supporting the recommendation that a red road traffic signal should have an intensity of 200 cd for optimum recognition from 100 m when the signal is seen against a very bright sky (104cd/m2). This confirmed the earlier result of Boisson and Pagès. The present paper extends the data to include (a) the effect of signal size on optimum signal intensity for a practical range of angular diameters (4.1 to 16.5 min of arc), and (b) the effect of background luminance for a range of luminances of 1.5 ft-L to 2250 ft-L. The results show that optimum signal intensity is independent of signal size and that spatial summation by the visual system is complete. However the same data demonstrate a failure of spatial summation when a conventional threshold criterion (probability of seeing the signal 0.5) is used. It is shown that smaller signals will be more effective than larger ones of the same intensity if their intensity is less than optimum. Optimum signal intensity is shown to be a linear function of background luminances greater than 10 ft-L. A graph relating optimum signal intensity to signalling range for various background luminances summarises the experimental data.

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

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
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