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Journal Radioengineering №4 for 2015 г.
Article in number:
Methods of processing video sequences based on the analysing the energy parameters of the video images
Authors:
A.V. Bogoslovsky - Dr. Sc. (Eng.), Professor, Department of Higher Mathematics, Tambov state technical University. E-mail: p-digim@mail.ru I.V. Zhigulina - Ph. D. (Eng.), Associate Professor, Department of Applied Mathematics and Mechanics, Tambov state technical University. E-mail: irazhigulina@gmail.com I.S. Maslov - Post-graduate Student, Department of Radio Engineering, Tambov state technical University. E-mail: klewer_tambov@mail.ru
Abstract:
When processing video sequences is of particular importance for effective detection of moving objects and determining motion parameters. As the use of energy spectra to determine the traffic is very inconvenient, for ease of use it is advisable to convert the differential energy spectra. Instead of the differential energy spectra can be considered the difference of the energy characteristics, which have greater visibility. More informative for determining the position of a moving object is the phase of the spatial spectrum. Since the use photocasting spectrum is also difficult, as the energy spectrum, it uses a phase-energy response. When working with real images to determine the position of a moving object in the frame is difficult using only the interframe differences between energy and phase-energy performances. Therefore, to improve the efficiency of detection of moving objects and determine the object\'s position and speed of its movement serves pixel reset the pixels of the image. After determining the position of the left edge of a moving object is consistent zeroing groups of pixels and a characteristic change frame-to-frame difference energy performance is determined by the amount of movement of a moving object. The resulting algorithms can effectively detect the moving object and the characteristics of its motion. Considered a limited number of processing algorithms. The use of frequency characteristics allows to realize a large number of modes due to the manipulation of data samples of the video signals.
Pages: 112-119
References

 

  1. Bogoslovsky A., Zhigulina I. A way of energy analysis for image and video sequence processing // Computer vision in control systems-1 / Favorskaya M.N., Jain L.C. (eds). Springer.BerlinHeidelberg. 2015. 183−210.
  2. Bogoslovskijj A.V., Bogoslovskijj E.A., ZHigulina I.V., JAkovlev V.A. Obrabotka mnogomernykh signalov. Linejjnaja mnogomernaja diskretnaja obrabotka signalov. Metody analiza i sinteza. M.: Radiotekhnika. 2013. 173 s.
  3. Bogoslovskijj A.V., ZHigulina I.V. Ispolzovanie fazochastotnykh prostranstvennykh kharakteristik dlja ocenki dvizhenija // Uspekhi sovremennojj radioehlektroniki. 2009. № 11. S. 61−63.
  4. Bogoslovskijj A.V., ZHigulina I.V., Kopylov O.E., JAkovlev V.A. Opredelenie parametrov dvizhenija obekta po izobrazheniju na osnove mezhkadrovykh raznostejj chastotnykh kharakteristik // Radiotekhnika. 2010. № 5. S. 55−59.
  5. Bogoslovskijj A.V., ZHigulina I.V., Kopylov O.E., JAkovlev V.A. Identifikacija dvizhushhikhsja obektov po mezhkadrovym raznostjam chastotnykh kharakteristik // Radiotekhnika. 2010. № 12. S. 55−60.
  6. Marcus E., RaichleTwo views of brain function // Trends in Cognitive Sciences. 2010. V. 14. № 4. P. 180−190.
  7. KHjubel D. Glaz, mozg, zrenie: Per. s angl. M.: Mir. 1990. 203 s.
  8. ZHigulina I.V., JAkovlev V.A. Analiz raboty biologicheski podobnykh algoritmov obnaruzhenija dvizhushhikhsja obektov // Voprosy sovremennojj nauki i praktiki (Universitet im. V.I. Vernadskogo). 2011. № 3(34). S. 41−56.