Advertisement

A Vehicle Logo Recognition Approach Based on Foreground-Background Pixel-Pair Feature

  • Zhenxing Nie
  • Ye YuEmail author
  • Qiang Jin
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10092)

Abstract

Traditional image features combined with different classifiers are widely used in existing vehicle logo recognition methods, which didn’t take into account the rich structure information of vehicle logos. Considering both their gray and structure information, a novel method based on foreground-background pixel pair (FBPP) feature, in which pixels are randomly sampled from foreground-background skeleton areas, is proposed. The pixel pair feature extraction process takes full consideration of vehicle logo structure, which makes this feature distinctive and discriminative. The experiment results show that, compared with methods based on features mainly focused on gray information, the method based on the proposed feature can achieve higher recognition performance. Especially under weak illumination, our method has shown strong robustness.

Keywords

Vehicle Logo Recognition Foreground-background Pixel pair feature Skeleton area Random sampling 

Notes

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61370167, 61305093), the Anhui Provincial Science and Technology Project (Grant No. 1401b042009).

References

  1. 1.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Psyllos, A.P., Anagnostopoulos, C.N.E., Kayafas, E.: Vehicle logo recognition using a sift-based enhanced matching scheme. IEEE Trans. Intell. Transp. Syst. 11(2), 322–328 (2010)CrossRefGoogle Scholar
  3. 3.
    Psyllos, A., Anagnostopoulos, C.N., Kayafas, E.: M-sift: a new method for vehicle logo recognition. In: 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp. 261–266. IEEE (2012)Google Scholar
  4. 4.
    Sidla, O., Kottmann, M., Benesova, W.: Real-time pose invariant logo and pattern detection. In: Proceedings of SPIE-The International Society for Optical Engineering, vol. 7878 (2011)Google Scholar
  5. 5.
    Wang, S.K., Liu, L., Xu, X.: Vehicle logo recognition based on local feature descriptor. Appl. Mech. Mater. Trans. Tech. Publ. 263, 2418–2421 (2013)Google Scholar
  6. 6.
    Yu, S., Zheng, S., Yang, H., et al.: Vehicle logo recognition based on bag-of-words. In: 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013, pp. 353–358. IEEE (2013)Google Scholar
  7. 7.
    Ou, Y., Zheng, H., Chen, S., et al.: Vehicle logo recognition based on a weighted spatial pyramid framework. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1238–1244. IEEE (2014)Google Scholar
  8. 8.
    Dai, S., Huang, H., Gao, Z., et al.: Vehicle-logo recognition method based on Tchebichef moment invariants and SVM. In: WRI World Congress on Software Engineering, WCSE 2009, vol. 3, pp. 18–21. IEEE (2009)Google Scholar
  9. 9.
    Sam, K.T., Tian, X.L.: Vehicle logo recognition using modest adaboost and radial Tchebichef moments. In: International Conference on Machine Learning and Computing, ICMLC 2012 (2012)Google Scholar
  10. 10.
    Xiao, J., Xiang, W., Liu, Y.: Vehicle logo recognition by weighted multi-class support vector machine ensembles based on sharpness histogram features. IET Image Process. 9(7), 527–534 (2015)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Liu, Z., Xiao, F.: A fast coarse-to-fine vehicle logo detection and recognition method. In: IEEE International Conference on Robotics and Biomimetics, ROBIO 2007, pp. 691–696. IEEE (2007)Google Scholar
  12. 12.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  13. 13.
    Llorca, D.F., Arroyo, R., Sotelo, M.A.: Vehicle logo recognition in traffic images using HOG features and SVM. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 2229–2234. IEEE (2013)Google Scholar
  14. 14.
    Peng, H., Wang, X., Wang, H., et al.: Recognition of low-resolution logos in vehicle images based on statistical random sparse distribution. IEEE Trans. Intell. Transp. Syst. 16(2), 681–691 (2015)Google Scholar
  15. 15.
    Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)CrossRefGoogle Scholar
  16. 16.
    Anakavej, T., Kawewong, A., Patanukhom, K.: Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients. In: 2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 179–186. IEEE (2013)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.VCC Division, School of Computer and InformationHefei University of TechnologyHefeiChina

Personalised recommendations