Improvement of mathematical methods for ensuring security based on real-time video sequence analysis

Purpose of research. Currently, mathematical methods of video sequence analysis represent a structured set of approaches to image recognition based on the difference in the glow of different image areas. Many of these values are described using mathematical dependencies, however, existing approaches work only for standard images obtained during video data processing. The purpose of this study is to develop a new approach to analyzing images obtained, including those using terahertz radiation, which has specific characteristics, both physical and mathematical.

Methods. The following theoretical and empirical scientific methods were used in this study. Analysis (the analysis of the currently known mathematical methods of image processing in order to recognize images is carried out). Synthesis (a fundamentally new approach to security systems is proposed, which is a single system consisting of separate interconnected subsystems). Modeling (an information model of a security system based on ACS has been developed using a system for analyzing and recognizing potentially dangerous objects based on a real-time video stream).

Mathematization (the image analysis system is described in the language of mathematical laws and formulas).

Results. As a result of the research based on the analysis of modern materials, the concept of a security system based on real-time video sequence analysis with the use of advanced object scanning technologies is proposed in the future. As the main innovation, an improved Viola-Jones image analysis method is proposed using an additional set characterizing the feature space of objects in the terahertz radiation range.

Conclusion. The use of high-frequency scanning technologies with intelligent object image recognition systems in real time will significantly reduce the risks of intruders entering protected facilities, as well as increase the safety of citizens with relatively low costs for the development and implementation of upgraded security systems.

1. Tiwari S., Singh A., Shukla V. Statistical moments based noise classification using feed forward back propagation neural network. Int. J. of Computer Applications. 2011; 18 (2): 36-40.

2. Alyautdinov M. A., Galushkin A. I., Kazantsev P. A., Ostapenko G. P. Neurocomputers. From software to hardware implementation. Moscow; 2016. 152 p. (In Russ.)

3. Bellman R., Dreyfus S. Applied problems of dynamic programming. Moscow: Nauka; 2016. 458 p. (In Russ.)

4. Brodetsky G.L. System analysis in logistics. The choice under many criteria. Moscow: Academia; 2015. 224 p. (In Russ.)

5. Bui Thi Thu Chang, Phan Ngoc Hoang, Spitsyn V.G. Face recognition based on the Viola–Jones method. Wavelet transform and principal component method. Izvestiya Tomskogo politekhnicheskogo universiteta = Proceedings of Tomsk Polytechnic University. 2012; 319(6): 54-59 (In Russ.)

6. Khan H., Yener B. Learning Filter Widths of Spectral Decompositions with Wavelets. Proc. of the NIPS Conf. 2018: 4601-4612.

7. Nikitin A. A., Limanova N. I. The process of image recognition by a neural network. Text: direct. Molodoi uchenyi = Young Scientist, 2020; (47): 23-25. (In Russ.)

8. Tanygin M. O., Alshaea H. Y. A., Dobritsa V. P., Dobroserdov O. G. Recursive Algorithm for Forming Structured Sets of Information Blocks to Increase the Speed of Their Source Determination Procedures. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University. 2021; 25(2): 51-64 (In Russ.). https://doi.org/10.21869/2223-1560-2021-25-2-51-64.

9. Trofimova E. A., Plotnikov S. V., Gilev D. V. Mathematical methods of analysis. Yekaterinburg: Ural University Press; 2015. 272 p. (In Russ.)

10. Konarev D. I., Gulamov A. A. Synthesis of Neural Network Architecture for Recognition of Sea-Going Ship Images. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University. 2020; 24(1): 130-143 (In Russ.). https://doi.org/10.21869/2223-1560-2020-24-1-130-143

11. Vdovin V.M., Surkova L.E., Valentinov V.A. Theory of systems and system analysis. Moscow: Dashkov and K; 2013. 644 p. (In Russ.)

12. Numerical Removal of Water-Vapor Effects from Hz-TDS Measurements: Withawat Withayachumnankul, Bernd M. Fischer, Samuel P. Mickan, Member, IEEE, and Derek Abbott, Fellow, IEEE; Oct, 2007.

13. Arkhipov A. E. Filtering of complex signals based on a two-level fuzzy-logic model. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University. 2023; 27(2): 140-154 (In Russ.). https://doi.org/10.21869/2223-1560-202327-2-140-154.

14. T-Ray Imaging: Daniel M. Mittleman, Rune H. Jacobsen, and Martin C. Nuss, Member. IEEE; IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS. September 1996; 2 (3).

15. Agafonov V.A. System analysis in strategic management. Moscow: Rusains; 2016. 48 p. (In Russ.)

16. Belov P.G. Risk management, system analysis and modeling]. Lyubertsy: Yurait; 2016. Part 1. 211 p. (In Russ.)

17. Savin S.I., Vorochaeva L.Yu., Malchikov А. V., Salikhzyanov А.М., Zalyaev E.М. Implementation Method of the Robot Adaptation to Contact Interaction Mode Changes Using Deep Fully Connected Neural Networks. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University. 2020; 24(1): 206-214 (In Russ.). https://doi.org/10.21869/2223-1560-2020-24-1-206-214.