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Rendering Scenes for Simulating Adverse Weather Conditions

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Advances in Computational Intelligence (IWANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12861))

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Abstract

Most of the object detection schemes do not perform well when the input image is captured in adverse weather. Reason being that the available datasets for training/testing of those schemes didn’t have many images in such weather conditions. Thus in this work, a novel approach to render foggy and rainy datasets is proposed. The rain is generated via estimation of the area of the scene image and then computing streak volume and finally overlapping the streaks with the scene image. As visibility reduces with depth due to fog, rendering of fog must take depth-map into consideration. In the proposed scheme, the depth map is generated from a single image. Then, the fog coefficient is generated by modifying the 3D Perlin noise with respect to the depth map. Further, blending the corresponding density of the fog with the scene image at a particular region based on precomputed intensities at that region. Demo dataset is available in this https://github.com/senprithwish1994/DatasetAdverse.

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Authors and Affiliations

  1. Indian Institute of Information Technology Guwahati, Guwahati, India

    Prithwish Sen, Anindita Das & Nilkanta Sahu

Authors
  1. Prithwish Sen
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  2. Anindita Das
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  3. Nilkanta Sahu
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Corresponding author

Correspondence to Prithwish Sen .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Málaga, Málaga, Spain

    Gonzalo Joya

  3. Technical University of Catalonia, Barcelona, Spain

    Andreu Català

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Sen, P., Das, A., Sahu, N. (2021). Rendering Scenes for Simulating Adverse Weather Conditions. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_29

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  • DOI: https://doi.org/10.1007/978-3-030-85030-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85029-6

  • Online ISBN: 978-3-030-85030-2

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