Machine Vision and Applications

, Volume 29, Issue 3, pp 393–403 | Cite as

Classify vehicles in traffic scene images with deformable part-based models

Original Paper


Vehicle classification is an important and challenging task in intelligent transportation systems, which has a wide range of applications. In this paper, we propose to integrate vehicle detection and vehicle classification into one single framework by using deformable part-based models. First of all, we use annotated vehicle images to train a deformable part-based model for each class of vehicles to be classified. Then, given a traffic scene image, we employ the obtained vehicle models to perform vehicle detection in it for vehicle extraction. After that, model alignment is performed on the extracted image crop, based on which features are extracted for creating a representation for the vehicle in the given image. We train a linear multi-class Support Vector Machine classifier based on representations of images in a validation set. Finally, we adopt the SVM classifier for vehicle classification. The proposed method is evaluated on the BIT-Vehicle Dataset, and can achieve an accuracy of \(91.08\%\), which is superior to methods used for comparison. Obtained results demonstrated the effectiveness of the proposed method.


Vehicle detection Vehicle classification Deformable part-based model Support Vector Machine Appearance feature 



This work was supported in part by National Natural Science Foundation of China (61602027).


  1. 1.
    Buch, N., Velastin, S.A., Orwell, J.: A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 12(3), 920–939 (2011)CrossRefGoogle Scholar
  2. 2.
    Chang, C.-C., Lin C.-J: Libsvm: A library for support vector machines. (2001)Google Scholar
  3. 3.
    Chen, Z., Ellis, T.: Semi-automatic annotation samples for vehicle type classification in urban environments. IET Intell. Transp. Syst. 3(9), 240–249 (2015)CrossRefGoogle Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of Computer Vision and Pattern Recognition, pp. 886–893, (2005)Google Scholar
  5. 5.
    Dong, Z., Wu, Y., Pei, M., Jia, Y.: Vehicle type classification using a semisupervised convolutional neural network. IEEE Trans. Intell. Transp. Syst. 16(4), 2247–2256 (2015)CrossRefGoogle Scholar
  6. 6.
    Farfade, S.S., Saberian, M., Li, L.-J: Multi-view face detection using deep convolutional neural networks. In: Proceedings of International Conference on Multimedia Retrieval, (2015)Google Scholar
  7. 7.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  8. 8.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (2014)Google Scholar
  9. 9.
    Gupte, S., Masoud, O., Martin, R.F.K., Papanikolopoulos, N.P.: Detection and classification of vehicles. IEEE Trans. Intell. Transp. Syst. 3(1), 37–47 (2002)CrossRefGoogle Scholar
  10. 10.
    Hsieh, J.-W., Yu, S.-H., Chen, Y.-S., Hu, W.-F.: Automatic traffic surveillance system for vehicle tracking and classification. IEEE Trans. Intell. Transp. Syst. 7(2), 175–187 (2006)CrossRefMATHGoogle Scholar
  11. 11.
    Jiang, M., Li, H.: Vehicle classification based on hierarchical support vector machine. In: Proceedings of the 2013 International Conference on Computer Engineering and Network, pp. 593–600, (2014)Google Scholar
  12. 12.
    Kafai, M., Bhanu, B.: Dynamic bayesian networks for vehicle classification in video. IEEE Trans. Ind. Inf. 8(1), 100–109 (2012)CrossRefGoogle Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)Google Scholar
  14. 14.
    Lai, A.H.S., Fung, G.S.K., Yung, N.H.C.: Vehicle type classification from visual-based dimension estimation. In Proceedings of IEEE International Conference on Intelligent Transportation Systems, pp. 201–206, (2001)Google Scholar
  15. 15.
    Liang, M., Huang, X., Chen, C.H., Chen, X.: Counting and classification of highway vehicles by regression analysis. IEEE Trans. Intell. Transp. Syst. 16(5), 2878–2888 (2015)CrossRefGoogle Scholar
  16. 16.
    Ma, X., Grimson, W.: Edge-based rich representation for vehicle classification. In: IEEE International Conference on Computer Vision, pp. 1185–1192, (2005)Google Scholar
  17. 17.
    Niknejad, H.T., Takeuchi, A., Mita, S., McAllester, D.: On-road multivehicle tracking using deformable object model and particle filter with improved likelihood estimation. IEEE Trans. Intell. Transp. Syst. 13(2), 748–758 (2012)CrossRefGoogle Scholar
  18. 18.
    Peng, Y., Jin, J.S., Luo, S., Xu, M., Au, S., Zhang, Z., Cui, Y.: Vehicle type classification using data mining techniques. In: The Era of Interactive Media, pp. 325–335, (2013)Google Scholar
  19. 19.
    Petrovic, V.S., Cootes, T.F: Analysis of features for rigid structure vehicle type recognition. In: British Machine Vision Conference, pp. 1–10Google Scholar
  20. 20.
    Psyllos, A., Anagnostopoulos, C.-N., Kayafas, E.: Vehicle model recognition from frontal view image measurements. Comput. Stand. Interfaces 33(2), 142–151 (2011)CrossRefGoogle Scholar
  21. 21.
    Shan, Y., Sawhney, H.S., Kumar, R.: Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras. IEEE Trans. Pattern Anal. Mach. Intell. 30(4), 700–711 (2008)CrossRefGoogle Scholar
  22. 22.
    Sivaraman, S., Trivedi, M.M.: Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans. Intell. Transp. Syst. 14(4), 1773–1795 (2013)CrossRefGoogle Scholar
  23. 23.
    Sivaraman, S., Trivedi, M.M.: Vehicle detection by independent parts for urban driver assistance. IEEE Trans. Intell. Transp. Syst. 14(4), 1597–1608 (2013)CrossRefGoogle Scholar
  24. 24.
    Szegedy, C., Liu, W., Jia, Y.Q., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9, (2015)Google Scholar
  25. 25.
    Zhang, B.: Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Trans. Intell. Transp. Syst. 14(1), 322–332 (2013)CrossRefGoogle Scholar
  26. 26.
    Zhang, Z., Tan, T., Huang, K., Wang, Y.: Three-dimensional deformable-model-based localization and recognition of road vehicles. IEEE Trans. Image Process. 21(1), 1–13 (2012)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  2. 2.Information CenterNational Natural Science Foundation of ChinaBeijingChina

Personalised recommendations