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Fine-Grained Vehicle Recognition in Traffic Surveillance

  • Qi Wang
  • Zhongyuan WangEmail author
  • Jing Xiao
  • Jun Xiao
  • Wenbin Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

Fine-grained vehicle recognition in traffic surveillance plays a crucial part in establishing intelligent transportation system. The major challenge lies in that differences among vehicle models are always subtle. In this paper, we propose a part-based method combining global and local feature for fine-grained vehicle recognition in traffic surveillance. We develop a novel voting mechanism to unify the preliminary recognition results, which are obtained by using Histograms of Oriented Gradients (HOG) and pre-trained convolutional neural networks (CNN), leading to fully exploiting the discriminative ability of different parts. Besides, we collect a comprehensive public database for 50 common vehicle models with manual annotation of parts, which is used to evaluate the proposed method and serves as supportive dataset for related work. The experiments show that the average recognition accuracy of our method can approach 92.3 %, which is 3.4 %–7.1 % higher than the state-of-art approaches.

Keywords

Fine-grained vehicle recognition CNN feature Discriminative parts Fine-grained vehicle dataset 

Notes

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (61502348), the EU FP7 QUICK project under Grant Agreement No. PIRSES-GA-2013-612652, China Postdoctoral Science Foundation funded project (2014M562058), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry ([2014]1685), the Fundamental Research Funds for the Central Universities (2042016gf0033), Natural Science Fund of Hubei Province (2015CFB406).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Qi Wang
    • 1
    • 2
  • Zhongyuan Wang
    • 1
    • 2
    • 3
    Email author
  • Jing Xiao
    • 1
    • 2
    • 3
  • Jun Xiao
    • 2
  • Wenbin Li
    • 2
  1. 1.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina
  2. 2.National Engineering Research Center for Multimedia SoftwareComputer School of Wuhan UniversityWuhanChina
  3. 3.Hubei Provincial Key Laboratory of Multimedia and Network Communication EngineeringWuhan UniversityWuhanChina

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