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Indoor vs. Outdoor Scene Classification for Mobile Robots

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Interactive Collaborative Robotics (ICR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12336))

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Abstract

This paper deals with the task of automatic indoor vs. outdoor classification from image data with respect to future usage in mobile robotics. For the requirements of this research, we utilize the Miniplaces dataset. We compare a large number of classic machine learning approaches such as Support Vector Machine, k-Nearest Neighbor, Decision Tree, or Naive Bayes using various color and texture description methods on a single dataset. Moreover, we employ some of the most important neural network-based approaches from the last four years. The best tested approach reaches 96.17% classification accuracy. To our best knowledge, this paper presents the most extensive comparison of classification approaches in the task of indoor vs. outdoor classification ever done on a single dataset. We also address the processing time problem, and we discuss using the applied methods in real-time robotic tasks.

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Notes

  1. 1.

    The rest of our results can be found in public GitHub repository: https://github.com/neduchal/io_classification_experiment.

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Acknowledgements

This work was supported by the Ministry of Education of the Czech Republic, project No. LTARF18017. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.

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Authors and Affiliations

  1. Faculty of Applied Sciences, New Technologies for the Information Society, University of West Bohemia, Univerzitní 8, 306 14, Plzeň, Czech Republic

    Petr Neduchal, Ivan Gruber & Miloš Železný

Authors
  1. Petr Neduchal
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  2. Ivan Gruber
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  3. Miloš Železný
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Corresponding author

Correspondence to Petr Neduchal .

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Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Andrey Ronzhin

  2. Technical University of Munich, Munich, Germany

    Gerhard Rigoll

  3. Institute of Control Sciences of the Russian Academy of Sciences, Moscow, Russia

    Roman Meshcheryakov

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Neduchal, P., Gruber, I., Železný, M. (2020). Indoor vs. Outdoor Scene Classification for Mobile Robots. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2020. Lecture Notes in Computer Science(), vol 12336. Springer, Cham. https://doi.org/10.1007/978-3-030-60337-3_24

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  • DOI: https://doi.org/10.1007/978-3-030-60337-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60336-6

  • Online ISBN: 978-3-030-60337-3

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