Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Simonyan K, Parkhi O, Vedaldi A et al. Fisher vector faces in the wild. In Proc. Conf. British Machine Vision, September 2013.

  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.

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  32. Kwang K, Keechul J, Hang J K. Face recognition using kernel principal component analysis. IEEE Signal Processing Letters, 2002, 9(2): 40-42.

    Article  Google Scholar 

  33. Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2014.

Download references

Publisher’s Note

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

Author information

Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200072, China

    Qian Wang

  2. Information Center, Criminal Investigation Department of Shanghai Public Security Bureau, Shanghai, 200083, China

    Qian Wang

  3. Shanghai Engineering Research Center of Motion Picture Special Effects, Shanghai, 200072, China

    Qian Wang & You-Dong Ding

  4. Shanghai Film Academy, Shanghai University, Shanghai, 200072, China

    You-Dong Ding

Authors
  1. Qian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. You-Dong Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qian Wang.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 752 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, ., Ding, YD. A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network. J. Comput. Sci. Technol. 33, 335–350 (2018). https://doi.org/10.1007/s11390-018-1822-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-018-1822-7

Keywords