Relevant Traffic Light Localization via Deep Regression

  • Rafael Freitas Federal University of Espirito Santo
  • Thiago Paixão Federal Institute of Espírito Santo
  • Rodrigo Berriel Universidade Federal do Espírito Santo
  • Alberto Souza Universidade Federal do Espírito Santo
  • Claudine Badue Federal University of Espirito Santo
  • Thiago Santos Universidade Federal do Espírito Santo

Resumo


Artificial intelligence advances have an important role on self-driving cars development, such as assisting the recognition of traffic lights. However, when relying on images of the scene alone, little progress was observed on selecting the traffic lights defining guidance to the car. Common detection approaches rely on additional high-level decision-making process to select a relevant traffic light. This work address the problem by proposing a deep regression system with an outliers resilient loss to predict the coordinates of a relevant traffic light in the image plane. The prediction can be used as a high-level decision-maker or as an assistant to a cheaper classifier to work on a region of interest. Results for European scenes show success in about 88% of the cases.

Palavras-chave: Traffic Light Recognition, Deep Learning, Self-driving Cars

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Publicado
15/10/2019
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FREITAS, Rafael; PAIXÃO, Thiago; BERRIEL, Rodrigo; SOUZA, Alberto; BADUE, Claudine; SANTOS, Thiago. Relevant Traffic Light Localization via Deep Regression. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 460-471. DOI: https://doi.org/10.5753/eniac.2019.9306.