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Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyCordeiroPedr:2018:ArCoRi
TitleAn Architecture for Collision Risk Prediction for Visually Impaired People
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size2069 KiB
Context area
Author1 Cordeiro, Natal Henrique
2 Pedrino, Emerson Carlos
Affiliation1 Federal Institute of São Paulo and Federal University of São Carlos
2 Federal University of São Carlos
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-08-28 21:23:22 :: -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
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Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsVisually impaired people, prediction, collision risk, dynamic objects.
AbstractThe production of sensory substitution equipment for the visually impaired (VIP) is growing. The aim of this project is to understand the VIP context and predict the risks of collision for the VIP, following an analysis of the position, distance, size and motion of the objects present in their environment. This understanding is refined by data fusion steps applied to the Situation Awareness model to predict possible impacts in the near future. With this goal, a new architecture was designed, composed of systems that detect free passages, static objects, dynamic objects and the paths of these dynamic objects. The detected data was mapped into a 3D plane verifying positions and sizes. For the fusion, a method was developed that compared four more general classifiers in order to verify which presented greater reliability in the given context. These classifiers allowed inferences to be made when analyzing the risks of collision in different directions. The architecture designed for risk prediction is the main contribution of this project.
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