Next Article in Journal
Smart Process Optimization and Adaptive Execution with Semantic Services in Cloud Manufacturing
Next Article in Special Issue
A Diabetes Management Information System with Glucose Prediction
Previous Article in Journal
Direction of Arrival Estimation Using Augmentation of Coprime Arrays
Previous Article in Special Issue
The Impact of Code Smells on Software Bugs: A Systematic Literature Review
Article Menu

Export Article

Open AccessArticle
Information 2018, 9(11), 278; https://doi.org/10.3390/info9110278

Prototyping a Traffic Light Recognition Device with Expert Knowledge

Programa de Pós-Graduação em Ciência da Computação, Universidade Federal de Sergipe, São Cristóvão 49100000, Brazil
*
Author to whom correspondence should be addressed.
Received: 27 September 2018 / Revised: 18 October 2018 / Accepted: 9 November 2018 / Published: 13 November 2018
(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))
Full-Text   |   PDF [3002 KB, uploaded 13 November 2018]   |  

Abstract

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%. View Full-Text
Keywords: traffic light detection and recognition; computer vision; expert systems; machine learning; support vector machines; adaptive background suppression filter; pcanet traffic light detection and recognition; computer vision; expert systems; machine learning; support vector machines; adaptive background suppression filter; pcanet
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Almeida, T.; Macedo, H.; Matos, L.; Vasconcelos, N. Prototyping a Traffic Light Recognition Device with Expert Knowledge. Information 2018, 9, 278.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top