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Background Suppression for Video Vehicle Tracking Systems with Moving Cameras Using Camera Motion Estimation

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Telematics in the Transport Environment (TST 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 329))

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Abstract

Camera oscillations and slight movements are typical in the video based parts of the Intelligent Transportation Systems, especially in the cases when the cameras are mounted on the high pylons or pillars, similarly as some street lamps. The influence of strong wind and some vibrations caused by some heavy vehicles may result in some shifts of the images captured as the consecutive video frames. In such situations some typical background estimation and removal algorithms based on the comparison of corresponding pixels of each video frame may lead to significant errors. The influence of such camera motions increases seriously for high focal length corresponding to tracking of distant objects. In order to minimize the influence of such movements the background suppression algorithm using the camera motion estimation is proposed in the paper increasing the stability of the estimated background which is further used in the vehicle tracking algorithm.

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References

  1. Klein, L.: Sensor Technologies and Data Requirements for ITS. Artech House ITS library, Norwood (2001)

    Google Scholar 

  2. Mazurek, P., Okarma, K.: Application of Bayesian a Priori Distributions for Vehicles’ Video Tracking Systems. In: Mikulski, J. (ed.) TST 2010. CCIS, vol. 104, pp. 347–355. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  3. Okarma, K., Mazurek, P.: Vehicle Tracking Using the High Dynamic Range Technology. In: Mikulski, J. (ed.) TST 2011. CCIS, vol. 239, pp. 172–179. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  4. Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (October 2004)

    Google Scholar 

  5. Reddy, V., Sanderson, C., Lovell, B.C.: A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts. EURASIP Journal on Image and Video Processing, Article ID 164956, 14 (2011)

    Google Scholar 

  6. Okarma, K., Mazurek, P.: Background estimation algorithm for optical car tracking applications. Machinebuilding and Electrical Engineering (7-8), 7–10 (2006)

    Google Scholar 

  7. Okarma, K., Mazurek, P.: Nonlinear background estimation methods for video vehicle tracking systems. Archives of Transport Systems Telematics 4(4), 42–48 (2011)

    Google Scholar 

  8. Okarma, K., Mazurek, P.: A modified hybrid method of nonlinear background estimation for vision based vehicle tracking systems. Logistyka (3), 1747–1752 (2012) (in Polish)

    Google Scholar 

  9. Okarma, K., Mazurek, P.: Application of Shape Analysis Techniques for the Classification of Vehicles. In: Mikulski, J. (ed.) TST 2010. CCIS, vol. 104, pp. 218–225. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House (1999)

    Google Scholar 

  11. Irani, M., Peleg, S.: Super resolution from image sequences. In: Proceedings of the International Conference on Pattern Recognition (ICPR), Atlantic City, New Jersey, vol. 2, pp. 115–120 (June 1990)

    Google Scholar 

  12. Irani, M., Peleg, S.: Improving resolution by image registration. Graphical Models and Image Processing 53, 231–239 (1991)

    Article  Google Scholar 

  13. Mazurek, P., Okarma, K.: Vehicle Tracking Using a Multi-scale Bayesian Algorithm for a Perspective Image of a Road. In: Mikulski, J. (ed.) TST 2011. CCIS, vol. 239, pp. 346–353. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Mazurek, P., Okarma, K. (2012). Background Suppression for Video Vehicle Tracking Systems with Moving Cameras Using Camera Motion Estimation. In: Mikulski, J. (eds) Telematics in the Transport Environment. TST 2012. Communications in Computer and Information Science, vol 329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34050-5_42

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  • DOI: https://doi.org/10.1007/978-3-642-34050-5_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34049-9

  • Online ISBN: 978-3-642-34050-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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