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Semantic Segmentation of Grey-Scale Traffic Scenes

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Book cover Image and Video Technology (PSIVT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11854))

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Abstract

In this paper we propose a novel architecture called KDNet that takes into account both spatial and temporal features for traffic scene labelling. One advantage of convolutional networks is the ability to yield hierarchical features that has been proved to bring high-quality result for scene labelling problem. We demonstrate that including temporal features has even better impact on segmenting the scene. We make use of convolutional long short-term memory cells in order to allow our model to take input at many different time steps. The backbone of our model is the well known fully convolutional network called FCN-8s. The model is built in an end-to-end manner, thus eliminating post-processing steps on its output. Our model outperforms FCN-8s at a significant margin on grey-scale video data.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. School of Engineering, Computer and Mathematical Sciences, Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand

    Trung Khoa Le & Reinhard Klette

Authors
  1. Trung Khoa Le
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  2. Reinhard Klette
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Correspondence to Trung Khoa Le .

Editor information

Editors and Affiliations

  1. Chonnam National University, Gwangju, Korea (Republic of)

    Chilwoo Lee

  2. Dalian University of Technology, Dalian, China

    Zhixun Su

  3. National Institute of Informatics, Tokyo, Japan

    Akihiro Sugimoto

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Le, T.K., Klette, R. (2019). Semantic Segmentation of Grey-Scale Traffic Scenes. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-34879-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34878-6

  • Online ISBN: 978-3-030-34879-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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