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Vehicle-Related Scene Understanding Using Deep Learning

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Pattern Recognition (ACPR 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1180))

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Abstract

Automated driving is an inevitable trend in future transportation, it is also one of the eminent achievements in the matter of artificial intelligence. Deep learning produces a significant contribution to the progression of automatic driving. In this paper, our goal is to primarily deal with the issue of vehicle-related scene understanding using deep learning. To the best of our knowledge, this is the first time that we utilize our traffic environment as an object for scene understanding based on deep learning. Moreover, automatic scene segmentation and object detection are joined for traffic scene understanding. The techniques based on deep learning dramatically decrease human manipulations. Furthermore, the datasets in this paper consist of a large amount of our collected traffic images. Meanwhile, the performance of our algorithms is verified by the experiential results.

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Authors and Affiliations

  1. Auckland University of Technology, Auckland, 1010, New Zealand

    Xiaoxu Liu, Minh Neuyen & Wei Qi Yan

Authors
  1. Xiaoxu Liu
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  2. Minh Neuyen
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  3. Wei Qi Yan
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Corresponding author

Correspondence to Wei Qi Yan .

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Editors and Affiliations

  1. University of Waikato, Hamilton, New Zealand

    Michael Cree

  2. National Ilan University, Yilan, Taiwan

    Fay Huang

  3. State University of New York at Buffalo, Buffalo, NY, USA

    Junsong Yuan

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Liu, X., Neuyen, M., Yan, W.Q. (2020). Vehicle-Related Scene Understanding Using Deep Learning. In: Cree, M., Huang, F., Yuan, J., Yan, W. (eds) Pattern Recognition. ACPR 2019. Communications in Computer and Information Science, vol 1180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3651-9_7

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  • DOI: https://doi.org/10.1007/978-981-15-3651-9_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3650-2

  • Online ISBN: 978-981-15-3651-9

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