Vehicle Type Detection by Convolutional Neural Networks

  • Miguel A. Molina-CabelloEmail author
  • Rafael Marcos Luque-Baena
  • Ezequiel López-Rubio
  • Karl Thurnhofer-Hemsi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)


In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations.


Foreground detection Background modeling Convolutional neural networks Probabilistic self-organizing maps Background features 



This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2014-53465-R, project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Andalusia (Spain) under projects TIC-6213, project name Development of Self-Organizing Neural Networks for Information Technologies; and TIC-657, project name Self-organizing systems and robust estimators for video surveillance. All of them include funds from the European Regional Development Fund (ERDF). Karl Thurnhofer-Hemsi is funded by a PhD scholarship from the Spanish Ministry of Education, Culture and Sport under the FPU program. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU.


  1. 1.
    Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., Vairo, C.: Deep learning for decentralized parking lot occupancy detection. Expert Syst. Appl. 72, 327–334 (2017)CrossRefGoogle Scholar
  2. 2.
    He, J., Tan, A.H., Tan, C.L., Sung, S.Y.: On quantitative evaluation of clustering systems. In: He, J., Tan, A.-H., Tan, C.-L., Sung, S.-Y. (eds.) Clustering and Information Retrieval, pp. 105–133. Springer, New York (2004)CrossRefGoogle Scholar
  3. 3.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Inc., Upper Saddle River (1988)zbMATHGoogle Scholar
  4. 4.
    Kato, N., Fadlullah, Z.M., Mao, B., Tang, F., Akashi, O., Inoue, T., Mizutani, K.: The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wirel. Commun. (2016)Google Scholar
  5. 5.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25, pp. 1097–1105 (2012)Google Scholar
  6. 6.
    López-Rubio, E., Luque-Baena, R.M.: Stochastic approximation for background modelling. Comput. Vis. Image Underst. 115(6), 735–749 (2011)CrossRefGoogle Scholar
  7. 7.
    Luque-Baena, R.M., López-Rubio, E., Domínguez, E., Palomo, E.J., Jerez, J.M.: A self-organizing map to improve vehicle detection in flow monitoring systems. Soft. Comput. 19(9), 2499–2509 (2015)CrossRefGoogle Scholar
  8. 8.
    Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Mithun, N., Howlader, T., Rahman, S.: Video-based tracking of vehicles using multiple time-spatial images. Expert Syst. Appl. 62, 17–31 (2016)CrossRefGoogle Scholar
  10. 10.
    Reid, D.: An algorithm for tracking multiple targets. IEEE Trans. Autom. Control 24(6), 843–854 (1979)CrossRefGoogle Scholar
  11. 11.
    Ren, J., Chen, Y., Xin, L., Shi, J., Li, B., Liu, Y.: Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment. IET Intel. Transport Syst. 10(6), 428–437 (2016)CrossRefGoogle Scholar
  12. 12.
    Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22(3), 400–407 (1951)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Sen-Ching, S.C., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Electronic Imaging 2004, pp. 881–892. International Society for Optics and Photonics (2004)Google Scholar
  14. 14.
    Wang, K., Liu, Y., Gou, C., Wang, F.Y.: A multi-view learning approach to foreground detection for traffic surveillance applications. IEEE Trans. Veh. Technol. 65(6), 4144–4158 (2016)CrossRefGoogle Scholar
  15. 15.
    Wren, C., Azarbayejani, A., Darrell, T., Pentl, A.: Pfinder real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)CrossRefGoogle Scholar
  16. 16.
    Wshah, S., Xu, B., Bulan, O., Kumar, J., Paul, P.: Deep learning architectures for domain adaptation in HOV/HOT lane enforcement. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–7 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Miguel A. Molina-Cabello
    • 1
    Email author
  • Rafael Marcos Luque-Baena
    • 1
  • Ezequiel López-Rubio
    • 1
  • Karl Thurnhofer-Hemsi
    • 1
  1. 1.Department of Computer Languages and Computer ScienceUniversity of MálagaMálagaSpain

Personalised recommendations