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A New SIFT-Based Camera Calibration Method for Hybrid Dual-Camera

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Informatics Engineering and Information Science (ICIEIS 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 252))

Abstract

Camera networks, consisting of various types of camera systems, play an important role in security surveillance system. This paper presents a new calibration method for hybrid multi-camera system; particularly, a video surveillance system with a static camera and a dynamic camera which is used for environment-monitoring and security purpose. The first static wide angle camera covers the complete scene, whereas the second dynamic camera, Pan-Tilt-Zoom (PTZ) camera provides multi-view-angle and multi-resolution images of the complete scene. The new proposed calibration method is based on Lowe’s Scale invariant Feature Transform (SIFT) algorithm and keypoints are selected based on the measurement of their stability. To improve the accuracy and robustness, a simple noise (unwanted keypoints) filtering technique using trigonometry theorem has also been adopted in the proposed system. From the obtained experimental results, it is shown that great improvement, in term of the determination and detection rate (from 55.71% to 94.87%) in camera networks calibration, has been achieved.

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References

  1. Liu, R., Zhang, H., Liu, M., Xia, X., Hu, T.: Stereo Cameras Self-calibration Based on SIFT. In: 2009 International Conference on Measuring Technology and Mechatronics Automation (2009), doi:10.1109/ICMTMA.2009.338

    Google Scholar 

  2. de Agapito, L., Hayman, E., Reid, I.D.: Self-calibration of rotating and zooming cameras. International Journal of Computer Vision 45(2) (November 2001)

    Google Scholar 

  3. Del Bimbo, A., Dini, F., Lisanti, G., Pernici, F.: Exploiting distinctive visual landmark maps in pan–tilt–zoom camera networks. Computer Vision and Image Understanding 114, 611–623 (2010)

    Article  Google Scholar 

  4. Bo, W., Nevatia, R.: Detection and Tracking of Multiple,Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors. International Journal of Computer Vision, doi:10.1007/s11263-006-0027-7

    Google Scholar 

  5. Zhou, H., Yuan, Y., Shi, C.: Object tracking using SIFT features and mean shift. Computer Vision and Image Understanding 113, 345–352 (2009)

    Article  Google Scholar 

  6. Lowe, D.G.: Object Recognition from Scale-Invariant Features. In: Proc. of International Conference on Computer Vision, Corfu, pp. 1150–1157 (September 1999)

    Google Scholar 

  7. Lindeberg, T.: Scale-space theory: a basic tool for analysing structures at different scales. J. Appl. Statist. 2(2), 224–270 (1994)

    Google Scholar 

  8. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision (2004)

    Google Scholar 

  9. Liu, J., Hubbold, R.: Automatic Camera Calibration and Scene Reconstruction with Scale-Invariant Features. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Nefian, A., Meenakshisundaram, G., Pascucci, V., Zara, J., Molineros, J., Theisel, H., Malzbender, T. (eds.) ISVC 2006. LNCS, vol. 4291, pp. 558–568. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Beis, J.S., Lowe, D.G.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: CVPR, p. 1000 (1997)

    Google Scholar 

  11. Ke, Y., Sukthankar, R.: PCA-SIFT, A more distinctive representation for local image descriptors. In: CVPR, pp. 506–513 (2004)

    Google Scholar 

  12. Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software 3(3), 209–226 (1977)

    Article  MATH  Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Low, YQ., Lee, SW., Goi, BM., Ng, MS. (2011). A New SIFT-Based Camera Calibration Method for Hybrid Dual-Camera. In: Abd Manaf, A., Zeki, A., Zamani, M., Chuprat, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25453-6_9

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  • DOI: https://doi.org/10.1007/978-3-642-25453-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25452-9

  • Online ISBN: 978-3-642-25453-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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