Real-Time Detection of Pedestrian Traffic Lights for Visually-Impaired People

Ghilardi, Marcelo Cabral; Simões, Gabriel; Wehrmann, Jônatas; Manssour, Isabel Harb; Barros, Rodrigo C.

doi:10.1109/ijcnn.2018.8489516

Cited by 10 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mascetti, Ahmetovic, Gerino, and Bernareggi (2016) and Mascetti, Ahmetovic, Gerino, and Bernareggi, et al (2016a, 2016b) proposed a reliable method of traffic light recognition using mobile devices and providing unsupervised transit for the visually impaired through image capturing and identification that enables them to gain visible traffic light images in a variety of lighting conditions due to time and weather. Ghilardi et al (2018) suggested a method for detecting pedestrian traffic lights and computer vision techniques based on deep neural networks and images captured by mobile devices after discovering a significant gap in the literature. Furthermore, Ivanchenko et al (2010) developed a prototype software application for mobile phones which uses computer vision algorithms to analyze video acquired by the built-in camera and then informs and warns the user in real-time when the Walk light illuminates.…”

Section: Related Work – History Of Traffic Lights Worldwide and Their...mentioning

confidence: 99%

Smart traffic lights for people with visual impairments: A literature overview and a proposed implementation

Theodorou

Μηλιώνης

Filios

2022

British Journal of Visual Impairment

View full text Add to dashboard Cite

Attempting to establish aids for individuals who are visually impaired has urged many cities to seek solutions for improving their quality of life. Namely, cities have installed sound-emitting devices into traffic lights as well as sidewalks that assist their navigation. Moreover, as cities are always striving to move forward and achieve innovations concerning navigation for disabled individuals, smart traffic lights, capable of synchronizing in real-time according to traffic and individual mobility conditions, are already being installed around the world. This is in line with the adoption of the smart city concept, which involves a set of methodologies and indicators that regulate how cities perform regarding the promotion of citizens’ quality of life. Another important principle is the techno-economic aspect indicating the need for low-cost careful planning to produce cost-efficient solutions, while additional important issues are maintenance, power efficiency, and the means to coordinate numerous devices to facilitate operation in a timely and reliable manner. In this article, we present an overview of the existing solutions for the navigation of people who are blind and visually impaired along with a requirement analysis performed on the feedback received from interviews with members of the Lighthouse for the Blind of Greece both of which lead to the proposal of a new implementation that pushes the state of the art.

show abstract

Section: Related Work – History Of Traffic Lights Worldwide and Their...mentioning

confidence: 99%

Smart traffic lights for people with visual impairments: A literature overview and a proposed implementation

Theodorou

Μηλιώνης

Filios

2022

British Journal of Visual Impairment

View full text Add to dashboard Cite

show abstract

“…The Faster R-CNN model was utilized to define the bounding box and its score [ 142 , 143 ]. Ghilardi et al [ 63 ] used alternative CNN architectures for the same purpose of traffic light detection and state classification. To detect small traffic lights, some architectures of deep learning are presented.…”

Section: Real-time Navigationmentioning

confidence: 99%

A Systematic Review of Urban Navigation Systems for Visually Impaired People

El-taher

Taha

Courtney

et al. 2021

Sensors

View full text Add to dashboard Cite

Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress.

show abstract

“…This model offers an integrated solution for localization and identification of tactile paving surfaces [14], detection of aerial and ground obstacles, and localization of crosswalks [15]. We also used deep neural networks for addressing: the development of an approach to classify different scenarios of indoor environments with or without doors and stairs [24]; the detection of pedestrian traffic lights together with their current state for helping visuallyimpaired people to cross the streets with the aid of their mobile devices [17].…”

Section: Computer Visionmentioning

confidence: 99%

“…In addition to research in data visualization and VA, we work with computer vision. We started by developing research projects aimed at assisting the mobility of visually impaired persons [14]- [17]. However, this year we started researching the use of computer vision for the security area.…”

Section: Introductionmentioning

confidence: 99%

DaVInt - Data Visualization and Interaction Lab

Manssour

Silveira²

2021

Anais Estendidos Da XXXIV Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2021)

View full text Add to dashboard Cite

A multidisciplinary research project between Communication and Computer Science researchers gave rise to the Data Visualization and Interaction Lab, known as DaVInt lab. Officially created in 2017, DaVInt comprises students and researchers from Communication, and Computer Science and related areas. DaVInt's main research topics are related to visual analytics of social media, narrative visualization, and data science. However, computer vision projects and other individual initiatives are also developed. Its main contributions are divided into three main areas: scientific, through our publications; technological, making the results of our researches available to the community; and student education, through which we disseminate the area beyond academia.

show abstract

Real-Time Detection of Pedestrian Traffic Lights for Visually-Impaired People

Cited by 10 publications

References 34 publications

Smart traffic lights for people with visual impairments: A literature overview and a proposed implementation

Smart traffic lights for people with visual impairments: A literature overview and a proposed implementation

A Systematic Review of Urban Navigation Systems for Visually Impaired People

DaVInt - Data Visualization and Interaction Lab

Contact Info

Product

Resources

About