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

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Abstract

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.

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Acknowledgements

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

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beijing Jiaotong University, No. 3 Shang Yuan Cun, Hai Dian District, Beijing, China

    Shuang Bai & Zhenyao Liu

  2. Information Center, National Natural Science Foundation of China, Beijing, China

    Chang Yao

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  1. Shuang Bai
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  2. Zhenyao Liu
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  3. Chang Yao
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Correspondence to Chang Yao.

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Bai, S., Liu, Z. & Yao, C. Classify vehicles in traffic scene images with deformable part-based models. Machine Vision and Applications 29, 393–403 (2018). https://doi.org/10.1007/s00138-017-0890-y

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  • DOI: https://doi.org/10.1007/s00138-017-0890-y

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