Machine Vision and Applications

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

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

  • Shuang Bai
  • Zhenyao Liu
  • Chang Yao
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).


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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

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