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D2TFRS: An Object Recognition Method for Autonomous Vehicles Based on RGB and Spatial Values of Pixels

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Abstract

Autonomous driving is now near future reality which will transform our world due to its numerous benefits. The foremost challenge to this task is to correctly identify the objects in the driving environment. In this work, we propose an object recognition method known as Decision Tree and Decision Fusion based Recognition System (D2TFRS) for autonomous driving. We combined two separate feature sets, which are RGB pixel values and spatial points X,Y of each pixel to form our dataset. The D2TFRS is based on our intuition that reclassification of pre-identified misclassified objects in a driving environment can give better prediction accuracy. Results showed that D2TFRS outperformed AdaBoost classifier and performed better than C5.0 classifier in terms of the classification accuracy and Kappa. In terms of speed, C5.0 outperforms both AdaBoost and D2TFRS. However, D2TFRS outperformed AdaBoost with respect to speed. We strongly believe that D2TFRS will have better parallelization performance compared to the other two methods and it will be investigated in our future work.

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Acknowledgements

The authors acknowledge with thanks the technical and financial support from the Deanship of Scientific Research (DSR) at the King Abdulaziz University (KAU), Jeddah, Saudi Arabia, under the grant number G-661-611-38. The work carried out in this paper is supported by the HPC Center at KAU.

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Furqan Alam & Iyad Katib

  2. High Performance Computing Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Rashid Mehmood

Authors
  1. Furqan Alam
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  2. Rashid Mehmood
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  3. Iyad Katib
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Corresponding author

Correspondence to Furqan Alam .

Editor information

Editors and Affiliations

  1. King Abdulaziz University, Jeddah, Saudi Arabia

    Rashid Mehmood

  2. Geographic Information Science and Technology, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Budhendra Bhaduri

  3. King Abdulaziz University, Jeddah, Saudi Arabia

    Iyad Katib

  4. European Alliance for Innovation, Bratislava, Slovakia

    Imrich Chlamtac

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Alam, F., Mehmood, R., Katib, I. (2018). D2TFRS: An Object Recognition Method for Autonomous Vehicles Based on RGB and Spatial Values of Pixels. In: Mehmood, R., Bhaduri, B., Katib, I., Chlamtac, I. (eds) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 224. Springer, Cham. https://doi.org/10.1007/978-3-319-94180-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-94180-6_16

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-94180-6

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