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A New Method of Stereo Localization Using Dual-PTZ-Cameras

  • Jing Xin
  • Xiaomin Ma
  • Yi Deng
  • Ding Liu
  • Han Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7508)

Abstract

To improve the localization accuracy and robustness of the moving 3D-target under the nature scenes, we propose a new target localization method through combining MSER (Maximally Stable Extremal Region) detector with SIFT (Scale Invariant Feature Transform) descriptor into the dual-PTZ-cameras stereo vision system. Firstly, stereo vision rectification is performed on the right-and-left images captured from the dual-PTZ-cameras with different focal lengths using designed Look-up-table(LUT )and BP neural network. Secondly, more high quality affine invariant features are extracted from the rectified images to perform initial matching using affine invariant feature detector and descriptor. Thirdly, erroneous correspondences is detected by RANSAC. Then, robust features matching under the multi-view-point and multi-focal-length is achieved. The localization experimental results of the moving 3-D target in a complex environment show that the proposed method has good localization accuracy and robustness.

Keywords

PTZ cameras stereo vision correction MSER detector SIFT descriptors 3D target localization 

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References

  1. 1.
    Kumar, S., Micheloni, C., Piciarelli, C.: Stereo Localization Using Dual PTZ Cameras. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 1061–1069. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  3. 3.
    Matas, J., Chum, O., Urban, M., et al.: Robust Wide-base Line Stereo from Maximally Stable Extremal Regions. Image and Vision Computing 22(10), 761–767 (2004)CrossRefGoogle Scholar
  4. 4.
    Samir, B.V.R., Na, S.I., Kalia, R.: Image Matching with SIFT descriptor on Affine Normalized MSERs. In: 17th Korea-Japan Joint Workshop on Korea-Japan Joint Workshop on Frontiers of Computer Vision, pp. 1–4 (2011)Google Scholar
  5. 5.
    Lin, G.Y., Zhang, W.G.: An Effective Robust Rectified Method for Stereo Vision. Journal of Image Graphic 11(2), 203–209 (2006)Google Scholar
  6. 6.
    Kumar, S., Micheloni, C., Piciarelli, C., Foresti, G.L.: Stereo Rectification of Uncalibrated and Heterogeneous images. Pattern Recognition Letters 31(11), 1445–1452 (2010)CrossRefGoogle Scholar
  7. 7.
    Kumar, S., Micheloni, C., Piciarelli, C., Foresti, G.L.: Stereo Localization based on Network’s Uncalibrated Camera Pairs. In: 6th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 502–507 (2009)Google Scholar
  8. 8.
    Forssen, P., Lowe, D.: Shape Descriptors for Maximally Stable Extremal Regions. In: 11th International Conference on Computer Vision, pp. 59–73 (2007)Google Scholar
  9. 9.
    Zhou, W.H., Du, X., Ye, X.Q.: Binocular Stereo Vision System Based on FPGA. Journal of Image Graphic 10(9), 1166–1170 (2005)Google Scholar
  10. 10.
    Zhang, Z.Y.: A F1exible New Technique for Camera Calibration. IEEE Trans on PAMI 22(11), 374–376 (2000)CrossRefGoogle Scholar
  11. 11.
    Rosten, E., Drummond, T.W.: Machine Learning for High-Speed Corner Detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jing Xin
    • 1
  • Xiaomin Ma
    • 1
  • Yi Deng
    • 1
  • Ding Liu
    • 1
  • Han Liu
    • 1
  1. 1.School of Information & AutomationXi’an University of TechnologyChina

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