Improved Tracking of Multiple Vehicles Using Invariant Feature-Based Matching

  • Jae-Young Choi
  • Jin-Woo Choi
  • Young-Kyu Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

Abstract

In case of monitoring road traffic,the image based monitoring system is more useful than any other system such as GPS or loop detector because it can give the whole picture of the two-dimensional traffic situation. The idea of this paper is that the quad-tree scheme segments MBR following from the background subtraction process. Then the segmented and detected vehicle regions, ROIs, are tracked by SIFT algorithm. Our method succeeded detecting and tracking multiple moving vehicles accurately in sequence frame. The proposed method is very useful for the video based applications such as automatic traffic monitoring system.

References

  1. 1.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  2. 2.
    Gepperth, A., Edelbrunner, J., Bucher, T.: Real-time detection and classification of cars in video sequences. In: Proc. Intelligent Vehicles Symposium, pp. 625–631 (2005)Google Scholar
  3. 3.
    Bay, H., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. In: European Conf. Computer Vision, pp. 404–417 (2006)Google Scholar
  4. 4.
    Carneiro, G., Jepson, A.: Multi-scale phase-based features. In: Int’l Conf. Computer Vision and Pattern Recognition, pp. 736–743 (2003)Google Scholar
  5. 5.
    Ha, D.-M., Lee, J.-M., Kim, Y.-D.: Neural-edge-based vehicle detection and traffic parameter extraction. Image and Vision Computing 22, 899–907 (2004)CrossRefGoogle Scholar
  6. 6.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. of the Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int’l J. Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Smith, J.R., Chang, S.-F.: Quad-tree segmentation for texture-based image query. In: Proc. ACM Int’l Conf. Multimedia, pp. 279–286 (1994)Google Scholar
  9. 9.
    Lindeberg, T.: Feature detection with automatic scale selection. Int’l J. Computer Vision 30(2), 77–116 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jae-Young Choi
    • 1
  • Jin-Woo Choi
    • 1
  • Young-Kyu Yang
    • 1
  1. 1.College of Software, Kyungwon University, Seongnam, Gyeonggi, 461-701Republic of Korea

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