Corner Detection-Based Image Feature Extraction and Description with Application to Target Tracking

  • Lejun Gong
  • Jiacheng Feng
  • Ronggen Yang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 348)


Image features extraction and description is very important for pattern recognition and image analysis. Corners in images are typical feature points and represent a lot of important information. Extracting corners accurately is significant to image processing, which can reduce much of the calculations. In this paper, a target tracking algorithm is developed which is based on the local invariant feature point extracting and representing with Harris-Laplace corner. The results of the experiments show the feasibility of the proposed method and accurately localize the target. At last it has been used to construct the intelligent transportation system.


Feature extraction Local invariant features Corner detection Target tracking 



This work is supported by the Natural Science Foundation of the Jiangsu Province (Project No. BK20130417), and Scientific Research Foundation for the introduction of talent of Nanjing University of Posts and Telecommunications (Project No. NY213088) and NUPTSF (Project No. NY214068).


  1. 1.
    Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Comput. Graphics Vis. 3(3), 177–280 (2007)Google Scholar
  2. 2.
    Colletto, F., Marcon, M., Sarti, A., Tubaro, S.: A robust method for the estimation of reliable wide baseline correspondences. In: Proceedings of the IEEE International Conference on Image Processing, pp. 1041–1044. Atlanta, GA, USA (2006)Google Scholar
  3. 3.
    Yang, Z., Guo, B.: Image mosaic based on SIFT. In: Proceedings of the International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1422–1425. Harbin, China (2008)Google Scholar
  4. 4.
    Vincent, E., Laganiere, R.: Detecting and matching feature points. J. Vis. Commun. Image Represent. 16(1), 38–54 (2005)CrossRefGoogle Scholar
  5. 5.
    Kim, T., Im, Y.J.: Automatic satellite image registration by combination of matching and random sample consensus. IEEE Trans. Geosci. Remote Sens. 41(5), 1111–1117 (2003)CrossRefGoogle Scholar
  6. 6.
    Suga, K., Fukuda, T., Takiguchi, Y.A.: Object recognition and segmentation using SIFT and graph cuts. In: Proceedings of the International Conference on Pattern Recognition, Tampa, Florida, USA (2008)Google Scholar
  7. 7.
    Liu, J., Chen, Z., Guo, R.: A mosaic method for aerial image sequence by R/C model. In: Proceedings of the International Conference on Computer Science and Software Engineering, pp. 58–61. Wuhan, China (2008)Google Scholar
  8. 8.
    Schmid, C., Mohr, R.: Local gray value invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 530–535 (1997)CrossRefGoogle Scholar
  9. 9.
    Chen, J., Zou, L., Zhang, J., et al.: The comparison and application of corner detection algorithms. J. Multimedia 4(6), 435–441 (2009)CrossRefGoogle Scholar
  10. 10.
    Mokhtarian, F., Mackworth, A.K.: A theory of multiscale, curvature based shape representation for planar curves. IEEE Trans. Pattern Anal. Mach. Intell. 14, 789–805 (1992)CrossRefGoogle Scholar
  11. 11.
    Zoghlami, O., Faugeras, R.D.: Using geometric corners to build a 2D mosaic from a set of images. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 420–425 (1997)Google Scholar
  12. 12.
    Wang, H., Brady, M.: Real-time corner detection algorithm for motion estimation. Image Vis. Comput. 13(9), 695–703 (1995)CrossRefGoogle Scholar
  13. 13.
    Yang, W., Dou, L., Zhang, J., Lu, J.: Automatic moving object detection and tracking in video sequences. In: SPIE Fifth International Symposium on Multispectral Image Processing and Pattern Recognition, pp. 676–712 (2007)Google Scholar
  14. 14.
    Vincent, E., Laganire, R.: Matching feature points in stereo pairs: a comparative study of some matching strategies. Mach. Graph. Vis. 10, 237–259 (2001)Google Scholar
  15. 15.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)MATHGoogle Scholar
  16. 16.
    Sarfraz, M., Asim, M.R., Masood, A.: Capturing outlines using cubic Bezier curves. In: Proceedings of IEEE 1st International Conference on Information and Communication Technologies: from Theory to Applications, pp. 539–540 (2004)Google Scholar
  17. 17.
    Cabrelli, C.A., Molter, U.M.: Automatic representation of binary images. IEEE Trans. Pattern Anal. Mach. Intell. 12(12), 1190–1196 (1990)CrossRefGoogle Scholar
  18. 18.
    Moravec, H.: Rover visual obstacle avoidance. In: Proceedings of the International Conference on Artificial Intelligence, pp. 785–790. Vancouver, Canada (1981)Google Scholar
  19. 19.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, pp. 147–151. Manchester, UK (1988)Google Scholar
  20. 20.
    Masood, A., Sarfraz, M.: Corner detection by sliding rectangles along planar curves. Comput. Graph. 31, 440–448 (2007)CrossRefGoogle Scholar
  21. 21.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vision 60, 63–86 (2004)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.School of Computer Science & Technology, School of SoftwareNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Faculty of Computer Science and TechnologyJinling Institute of TechnologyNanjingChina

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