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Ground Plane Segmentation Using Artificial Neural Network for Pedestrian Detection

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Image Analysis and Recognition (ICIAR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10317))

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Abstract

This paper presents a method of ground plane segmentation for urban outdoor scenes using a feedforward artificial neural network (ANN). The main motivation of this project is to obtain some contextual information from the scene for use in pedestrian detection algorithms and to provide an accuracy improvement for this algorithms. The ANN input is fed with features extracted from a patch window of the image scene. The ANN output classifies the patch as belonging or not belonging to the ground plane. After that, the classified patches are joined into a full image with the ground plane area outlined. The images used for training, test and evaluation were obtained from the widely known Caltech-USA database. The accuracy of ground plane segmentation was above 96% in the experiments which improved the precision of the pedestrian detector in 38,5%.

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Authors and Affiliations

  1. Universidade Presbiteriana Mackenzie, São Paulo, SP, Brazil

    Jorge Candido & Mauricio Marengoni

Authors
  1. Jorge Candido
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  2. Mauricio Marengoni
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Corresponding author

Correspondence to Jorge Candido .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. Politechnique Montreal, Montreal, Québec, Canada

    Farida Cheriet

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Candido, J., Marengoni, M. (2017). Ground Plane Segmentation Using Artificial Neural Network for Pedestrian Detection. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_30

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  • DOI: https://doi.org/10.1007/978-3-319-59876-5_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59875-8

  • Online ISBN: 978-3-319-59876-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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