doi: 10.17586/2226-1494-2018-18-3-487-492


M. V. Zakharova, G. M. Shmyhelskiy, V. V. Grigoriev

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For citation: Zakharova M.V., Shmyhelskyi G., Grigoriev V.V. Study of computer vision algorithms for space tracking systems in typical modes of their functioning. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 3, pp. 487–492 (in Russian). doi: 10.17586/2226-1494-2018-18-3-487-492


 The paper proposes an algorithm for an object capturing and tracking in a frame for automatic phase focusing of a single-objective camera with a mirror obturator. The capture is performed by the object identification through the filtering of boundaries and edges by the Canny operator; then the Hough transformation is used to detect the characteristic lines of the object. Reliable support of the object is provided by a predictor/equalizer based on the Kalman filter. The proposed calculation algorithm makes it possible to achieve optimal performance, sufficient for the object capturing and tracking in the frame and its timely exposure. This algorithm was implemented to track an object within the scene, the trajectory and speed of which are unknown in advance that excludes the possibility of creation a self-learning algorithm. The Canny operator provides the most accurate detection of object boundaries from existing filtering methods that simplifies the subsequent image processing. The application of an additional Hough transformation makes it possible to speed up the calculations by reducing the amount of data processed, confirming the overall speed of the algorithm as compared to the classical filtering method. The usage of the Kalman filter as a predictor/equalizer  gives the possibility to pre-determine the point for focusing at the next time. The proposed calculation algorithm makes it possible to achieve optimal performance, sufficient for the object capturing and tracking on the stage, and also sufficient for timely exposure of the frame.

Keywords: tracking system, boundary detector, object tracking, Hough transformation, Kalman filter

Acknowledgements. This work was supported by the Ministry of Education and Science of the Russian Federation (Project 14.Z50.31.0031)

  1. Gonzales R.C., Woods R.E. Digital Image Processing. 2nd ed. Upper Saddle River, Prentice Hall, 2002, 793 p.
  2. Makarov M.A. Contour analisys in the problems of description and classification of objects. Sovremennye Problemy Nauki i Obrazovaniya, 2014, no. 3, p. 38. (in Russian)
  3. D’yakonov V., Abramenkova I. MATLAB. Processing of Signals and Images. St. Petersburg, Piter Publ., 2002, 608 p. (In Russian)
  4. Canny J.F. Finding Edges and Lines in Images. Artificial Intelligence Laboratory Technical Report Al-TR-720. Cambridge, Massachusetts Institute of Technology, 1983.
  5. Makarov M.A., Berestneva O.G., Andreev S.Yu. Solving the problem of moving objects contour classification and recognition on video frame. Bulletin of the Tomsk Polytechnic University, 2014, vol. 325, no. 5, pp. 77–83. (in Russian)
  6. Pentland A.P. Visual Inference of Shape: Computation from Local Features. PhD dissertation. Cambridge, Massachusetts Inst. Technol., 1982.
  7. Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, vol. PAMI-8, no. 6, pp. 679–698. doi: 10.1109/TPAMI.1986.4767851
  8. Hough P.V.C. Method and means for recognizing complex patterns. Patent US3069654, 1962.
  9. D'Orazio T., Guaragnella C., Leo M., Distante A. A new algorithm for ball recognition using circle Hough transform and neural classifier. Pattern Recognition, 2004, vol. 37, no. 3, pp. 393–408. doi: 10.1016/S0031-3203(03)00228-0
  10. Balchen J.G., Jenssen N.A., Saelid S. Dynamic positioning using Kalman filtering and optimal control theory. Proc. IFAC/IFIP Symposium on Automation in Offshore Oil Field Operation. Bergen, Norway, 1976, pp. 183–186.
  11. Kalman R.E., Bucy R.S. New results in linear filtering and prediction theory. Journal of Fluids Engineering, Transactions of the ASME, 1961, vol. 83, pp. 95–108. doi: 10.1115/1.3658902
  12. Besekerskii V.A., Popov E.P. The Theory of Automatic Control Systems. Moscow, Nauka Publ., 1975, 768 p. (in Russian)
  13. Gonsales R.C., Woods R.E., Eddins S.L. Digital Image Processing Using MATLAB. Prentice Hall, 2004, 344 p.
  14. Kolyuchkin V.Ya., Nguen C.M., Tran T.H. Image processing algorithms in machine vision systems of robotized industrial lines. Journal Neurocomputers, 2014, no. 3, pp. 44–51. (in Russian)
  15. Long M., Peng F. A box-counting method with adaptable box height for measuring the fractal feature of images. Radioengineering, 2013, vol. 22, pp. 208–213.
  16. Perreault S., Hebert P. Median filtering in constant time. IEEE Transactions on Image Processing, 2007, vol. 16, no. 9, pp. 2389–2394. doi: 10.1109/TIP.2007.902329
  17. Lee P.M., Chen H.Y. Adjustable gamma correction circuit for TFT LCD. Proc. IEEE International Symposium on Circuits and Systems. Kobe, Japan, 2005, pp. 780–783. doi: 10.1109/ISCAS.2005.1464704

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