Advertisement

Journal of Computer Science and Technology

, Volume 33, Issue 2, pp 335–350 | Cite as

A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network

  • Qian Wang
  • You-Dong Ding
Regular Paper
  • 47 Downloads

Abstract

In this paper, we propose a locally enhanced PCANet neural network for fine-grained classification of vehicles. The proposed method adopts the PCANet unsupervised network with a smaller number of layers and simple parameters compared with the majority of state-of-the-art machine learning methods. It simplifies calculation steps and manual labeling, and enables vehicle types to be recognized without time-consuming training. Experimental results show that compared with the traditional pattern recognition methods and the multi-layer CNN methods, the proposed method achieves optimal balance in terms of varying scales of sample libraries, angle deviations, and training speed. It also indicates that introducing appropriate local features that have different scales from the general feature is very instrumental in improving recognition rate. The 7-angle in 180° (12-angle in 360°) classification modeling scheme is proven to be an effective approach, which can solve the problem of suffering decrease in recognition rate due to angle deviations, and add the recognition accuracy in practice.

Keywords

fine-grained classification PCANet local enhancement vehicle type recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

11390_2018_1822_MOESM1_ESM.pdf (753 kb)
ESM 1 (PDF 752 kb)

References

  1. 1.
    Simonyan K, Parkhi O, Vedaldi A et al. Fisher vector faces in the wild. In Proc. Conf. British Machine Vision, September 2013.Google Scholar
  2. 2.
    Berg T, Belhumeur P N. POOF: Part-based one-vs-one features for fine-grained categorization, face verification, and attribute estimation. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2013, pp.955-962.Google Scholar
  3. 3.
    Cao Q, Ying Y, Li P. Similarity metric learning for face recognition. In Proc. IEEE Int. Conf. Computer Vision, January 2013, pp.2408-2415.Google Scholar
  4. 4.
    Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10 000 classes. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2014, pp.1891-1898.Google Scholar
  5. 5.
    Sun Y, Chen Y, Wang X et al. Deep learning face representation by joint identification-verification. In Proc. Int. Conf. Neural Information Processing Systems, November 2015, pp.1988-1996.Google Scholar
  6. 6.
    Feris R S, Siddiquie B, Petterson J et al. Large-scale vehicle detection, indexing, and search in urban surveillance videos. IEEE Trans. Multimedia, 2012, 14(1): 28-42.CrossRefGoogle Scholar
  7. 7.
    Hu C, Bai X, Qi L et al. Learning discriminative pattern for real-time car brand recognition. IEEE Trans. Intelligent Transportation Systems, 2015, 16(6):3170-3181.CrossRefGoogle Scholar
  8. 8.
    Grauman K, Crandall D, Parikh D et al. Discovering localized attributes for fine-grained recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2012, pp.3474-3481.Google Scholar
  9. 9.
    Wah C, Horn G V, Branson S et al. Similarity comparisons for interactive fine-grained categorization. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2014, pp.859-866.Google Scholar
  10. 10.
    Goering C, Rodner E, Freytag A et al. Nonparametric part transfer for fine-grained recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2014, pp.2489-2496.Google Scholar
  11. 11.
    Krause J, Deng J, Stark M et al. Collecting a large-scale dataset of fine-grained cars. In Proc. the 2nd Fine-Grained Visual Categorization Workshop, June 2013.Google Scholar
  12. 12.
    Yang L, Luo P, Chen C L et al. A large-scale car dataset for fine-grained categorization and verification. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2015, pp.3973-3981.Google Scholar
  13. 13.
    Krause J, Stark M, Deng J et al. 3D object representations for fine-grained categorization. In Proc. IEEE Int. Conf. Computer Vision, June 2013, pp.554-561.Google Scholar
  14. 14.
    Lin Y L, Morariu V I, Hsu W et al. Jointly optimizing 3D model fitting and fine-grained classification. In Proc. European Conference on Computer Vision, September 2014, pp.466-480.Google Scholar
  15. 15.
    Stark M, Krause J, Pepik B et al. Fine-grained categorization for 3D scene understanding. In Proc. Conf. British Machine Vision, September 2012, pp.228-236.Google Scholar
  16. 16.
    Sochor J, Herout A, Havel J. BoxCars: 3D boxes as CNN input for improved fine-grained vehicle recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2016, pp.3006-3015.Google Scholar
  17. 17.
    Zhang X, Zhou F, Lin Y et al. Embedding label structures for fine-grained feature representation. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2016, pp.1114-1123.Google Scholar
  18. 18.
    Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In Proc. Int. Conf. Neural Information Processing Systems, November 2012, pp.1097-1105.Google Scholar
  19. 19.
    He H, Shao Z, Tan J. Recognition of car makes and models from a single traffic-camera image. IEEE Trans. Intelligent Transportation Systems, 2015, 16(6): 3182-3192.CrossRefGoogle Scholar
  20. 20.
    Chan T H, Jia K, Gao S et al. PCANet: A simple deep learning baseline for image classification? IEEE Trans. Image Processing, 2014, 24(12): 5017-5032.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Dong Z, Wu Y, Pei M et al. Vehicle type classification using a semi supervised convolutional neural network. IEEE Trans. Intelligent Transportation Systems, 2015, 16(4): 2247-2256.CrossRefGoogle Scholar
  22. 22.
    Xie S, Yang T, Wang X et al. Hyper-class augmented and regularized deep learning for fine-grained image classification. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2015, pp.2645-2654.Google Scholar
  23. 23.
    Zhao B, Wu X, Feng J et al. Diversified visual attention networks for fine-grained object classification. IEEE Trans. Multimedia, 2017, 19(6): 1245-1256.CrossRefGoogle Scholar
  24. 24.
    Zia M Z, Stark M, Schindler K. Towards scene understanding with detailed 3D object representations. International Journal of Computer Vision, 2015, 112(2): 188-203.MathSciNetCrossRefGoogle Scholar
  25. 25.
    Arandjelovic R, Zisserman A. Three things everyone should know to improve object retrieval. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2012, pp.2911-2918.Google Scholar
  26. 26.
    Dalal N, Triggs B. Histograms of oriented gradients for human detection. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2005, pp.886-893.Google Scholar
  27. 27.
    Chen T, Chen Z, Shi Q et al. Road marking detection and classification using machine learning algorithms. In Proc. Intelligent Vehicles Symp., June 2015, pp.617-621.Google Scholar
  28. 28.
    Wang X S, Cai C. Weed seeds classification based on PCANet deep learning baseline. In Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, December 2015, pp.408-415.Google Scholar
  29. 29.
    Wu J, Shi J, Li Y et al. Histopathological image classification using random binary hashing based PCANet and bilinear classifier. In Proc. Conf. European Signal Processing, August 2016, pp.2050-2054.Google Scholar
  30. 30.
    Xia Y, Li J, Qi L et al. Loop closure detection for visual SLAM using PCANet features. In Proc. Int. Conf. Neural Networks, July 2016, pp.2274-2281.Google Scholar
  31. 31.
    Jia H, Sun Q, Wang T. PCANet for blind image quality assessment. In Proc. Int. Conf. Computational Intelligence and Security, December 2015, pp.195-198.Google Scholar
  32. 32.
    Kwang K, Keechul J, Hang J K. Face recognition using kernel principal component analysis. IEEE Signal Processing Letters, 2002, 9(2): 40-42.CrossRefGoogle Scholar
  33. 33.
    Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2014.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina
  2. 2.Information Center, Criminal Investigation Department of Shanghai Public Security BureauShanghaiChina
  3. 3.Shanghai Engineering Research Center of Motion Picture Special EffectsShanghaiChina
  4. 4.Shanghai Film AcademyShanghai UniversityShanghaiChina

Personalised recommendations