<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>3202</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>262</pageFirst>
    <pageLast>271</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>SciTePress</publisherName>
    <publisherPlace>Setúbal</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploiting GAN Capacity to Generate Synthetic Automotive Radar Data</title>
    <abstract language="eng">In this paper, we evaluate the training of GAN for synthetic RAD image generation for four objects reflected by Frequency Modulated Continuous Wave radar: car, motorcycle, pedestrian and truck. This evaluation adds a new possibility for data augmentation when radar data labeling available is not enough. The results show that, yes, the GAN generated RAD images well, even when a specific class of the object is necessary. We also compared the scores of three GAN architectures, GAN Vanilla, CGAN, and DCGAN, in RAD synthetic imaging generation. We show that the generator can produce RAD images well enough with the results analyzed.</abstract>
    <parentTitle language="eng">Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4</parentTitle>
    <identifier type="isbn">978-989-758-634-7</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-32029</identifier>
    <enrichment key="THI_conferenceName">18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Lisbon (Portugal), 19.-21.02.2023</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.5220/0011672400003417</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Mauren Louise S. C.</first_name>
      <last_name>de Andrade</last_name>
    </author>
    <editor>
      <first_name>Petia</first_name>
      <last_name>Radeva</last_name>
    </editor>
    <author>
      <first_name>Matheus</first_name>
      <last_name>Velloso Nogueira</last_name>
    </author>
    <editor>
      <first_name>Giovanni Maria</first_name>
      <last_name>Farinella</last_name>
    </editor>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Fidelis</last_name>
    </author>
    <editor>
      <first_name>Kadi</first_name>
      <last_name>Bouatouch</last_name>
    </editor>
    <author>
      <first_name>Luiz Henrique</first_name>
      <last_name>Aguiar Campos</last_name>
    </author>
    <author>
      <first_name>Pietro</first_name>
      <last_name>Campos</last_name>
    </author>
    <author>
      <first_name>Torsten</first_name>
      <last_name>Schön</last_name>
    </author>
    <author>
      <first_name>Lester</first_name>
      <last_name>de Abreu Faria</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Radar Application</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Generative Adversarial Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ground-Based Radar Dataset</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Synthetic Automotive Radar Data</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="41270">Schön, Torsten</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3202/116724.pdf</file>
  </doc>
</export-example>
