Conditional transfer with dense residual attention: synthesizing traffic signs from street-view imagery

Clint Sebastian, Ries Uittenbogaard, Julien Viiverberg, Bas Boom, Peter H.N. De With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
63 Downloads (Pure)

Abstract

Object detection and classification of traffic signs in street-view imagery is an essential element for asset management, map making and autonomous driving. However, some traffic signs occur rarely and consequently, they are difficult to recognize automatically. To improve the detection and classification rates, we propose to generate images of traffic signs, which are then used to train a detector/classifier. In this research, we present an end-to-end framework that generates a realistic image of a traffic sign from a given image of a traffic sign and a pictogram of the target class. We propose a residual attention mechanism with dense concatenation called Dense Residual Attention, that preserves the background information while transferring the object information. We also propose to utilize multi-scale discriminators, so that the smaller scales of the output guide the higher resolution output. We have performed detection and classification tests across a large number of traffic sign classes, by training the detector using the combination of real and generated data. The newly trained model reduces the number of false positives by 1.2 - 1.5% at 99% recall in the detection tests and an absolute improvement of 4.65% (top-l accuracy) in the classification tests.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages553-559
Number of pages7
ISBN (Electronic)978-1-5386-3788-3
DOIs
Publication statusPublished - 26 Nov 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 20 Aug 201824 Aug 2018

Conference

Conference24th International Conference on Pattern Recognition, ICPR 2018
CountryChina
CityBeijing
Period20/08/1824/08/18

Keywords

  • Generative adversarial networks
  • Deep learning

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