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International Conference on Hybrid Artificial Intelligence Systems

HAIS 2019: Hybrid Artificial Intelligent Systems pp 264–274Cite as

Classification of Human Driving Behaviour Images Using Convolutional Neural Network Architecture

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11734))

Abstract

Traffic safety is a problem that concerns the worldwide. Many traffic accidents occur. There are many situations that cause these accidents. However, when we look at the relevant statistics, it is seen that the traffic accident is caused by the behavior of the driver. Drivers who exhibit careless behavior, cause an accident. Preliminary detection of such actions may prevent the accident. In this study, it is possible to recognize the behavior of the state farm distracted driver detection data, which includes nine situations and one normal state image, which may cause an accident. The images are preprocessed with the LOG (Laplasian of Gaussian) filter. The feature extraction process is carried out with googlenet, which is the convolutional neural network architecture. As a result, the classification process resulted in 97.7% accuracy.

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Author information

Authors and Affiliations

  1. Firat University, 23800, Elazig, Turkey

    Emine Cengil & Ahmet Cinar

Authors
  1. Emine Cengil
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  2. Ahmet Cinar
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Corresponding author

Correspondence to Emine Cengil .

Editor information

Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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Cite this paper

Cengil, E., Cinar, A. (2019). Classification of Human Driving Behaviour Images Using Convolutional Neural Network Architecture. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_23

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29858-6

  • Online ISBN: 978-3-030-29859-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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