Vehicle Categorical Recognition for Traffic Monitoring in Intelligent Transportation Systems

  • Diem-Phuc Tran
  • Van-Dung HoangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)


Automatic vehicle detection and recognition play a vital role in intelligent transport systems (ITS). However, study results in this field remain certain limitations in terms of accuracy and processing time. This article proposes a solution to improve the accuracy of vehicle recognition in order to support traffic monitoring on vehicle restricted roads. The proposed solution to vehicle recognition consists of two basic stages: (1) Vehicle detection, (2) vehicle recognition. This study focuses on proposing solutions for improving the accuracy of vehicle recognition (stage 2). The vehicle recognition solution is based on the combination of architectural development in deep neural networks, SVM model, and data augmenting solutions. It aims at achieving a greater accuracy than traditional approaches. The proposed solution is experimented, evaluated, and compared with different approaches to the same set of data. Experimental results have shown that the proposed solution brings a higher accuracy than other approaches. Along with an acceptable processing time, this promising solution is able to be applied in practical systems.


Deep learning Vehicle recognition Feature extraction 


  1. 1.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS 2012), vol. 25, pp. 1106–1114 (2012)Google Scholar
  2. 2.
    Amirullah, I., Bakti, R.Y., Areni, I., Alimuddin, A.A.: Vehicle detection and tracking using Gaussian Mixture Model and Kalman filter, pp. 115–119 (2016)Google Scholar
  3. 3.
    Chen, X., Xiang, S., Liu, C.-L., Pan, C.-H.: Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 11, 1797–1801 (2014)CrossRefGoogle Scholar
  4. 4.
    Chen, Y., Wu, Q.: Moving vehicle detection based on optical flow estimation of edge, pp. 754–758 (2015)Google Scholar
  5. 5.
    Choi, J.-y., Sung, K.-S., Yang, Y.: Multiple vehicles detection and tracking based on scale-invariant feature transform, pp. 528–533 (2007)Google Scholar
  6. 6.
    Espinosa, J.E., Velastin, S.A., Branch, J.W.: Vehicle detection using alex net and faster R-CNN deep learning models: a comparative study. In: Badioze Zaman, H., et al. (eds.) Advances in Visual Informatics. IVIC 2017. LNCS, vol. 10645, pp. 3–15. Springer, Cham (2017). Scholar
  7. 7.
    da Silva Filho, S.G., Freire, R.Z., dos Santos Coelho, L.: Feature extraction for on-road vehicle detection based on support vector machine. In: Conference Proceedings (2017)Google Scholar
  8. 8.
    Girshick, R.: Fast R-CNN (2015)Google Scholar
  9. 9.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2014)Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, vol. 1502 (2015)Google Scholar
  11. 11.
    He, K., Zhang, X., Ren, S, Sun, J.: Deep residual learning for image recognition, pp. 770–778 (2016)Google Scholar
  12. 12.
    Koga, Y., Miyazaki, H., Shibasaki, R.: Counting vehicles by deep neural network in high resolution satellite images (2017)Google Scholar
  13. 13.
    Bautista, C.M., Dy, C.A., Manalac, M.I., Orbe, R.A., Cordel II, M.: Convolutional neural network for vehicle detection in low resolution traffic videos, pp. 277–281 (2016)Google Scholar
  14. 14.
    Moutakki, Z., Ouloul, M.I., Afdel, K., Amghar, A.: Real-time system based on feature extraction for vehicle detection and classification. Transp. Telecommun. J. 19, 93–102 (2018)CrossRefGoogle Scholar
  15. 15.
    Qu, S., Wang, Y., Meng, G., Pan, C.: Vehicle detection in satellite images by incorporating objectness and convolutional neural network (2016)Google Scholar
  16. 16.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks, pp. 1–10 (2016)Google Scholar
  17. 17.
    Szegedy, C., et al.: Going deeper with convolutions, pp. 1–9 (2015)Google Scholar
  18. 18.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481–2495 (2017)CrossRefGoogle Scholar
  19. 19.
    Yan, G., Ming, Y., Yu, Y., Fan, L.: Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification (2016)Google Scholar
  20. 20.
    Yılmaz, A., Guzel, M., Askerbeyli, I., Bostanci, E.: A vehicle detection approach using deep learning methodologies (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Duy Tan UniversityDa NangVietnam
  2. 2.Quang Binh UniversityĐồng HớiVietnam

Personalised recommendations