THE METHOD OF OBSERVING MOVING OBJECTS

Authors

  • D. Mezin
  • N. Kuchuk
  • A. Lyashova
  • S. Partyka
  • D. Lysytsia

DOI:

https://doi.org/10.26906/SUNZ.2024.2.122

Keywords:

tracking, moving objects, pattern recognition, computing system, logical blocks

Abstract

The article analyzes known algorithms for tracking moving objects. Based on an analysis of known algorithms for tracking moving objects, it was concluded that the best tracking quality in problems with a large number of observed objects is achieved by solutions built on the basis of probabilistic and hierarchical methods. Each of them has complementary advantages, which creates prospects for creating new algorithmic solutions built on the synergy of these approaches. The main task of promising tracking methods is that they should provide ease of scaling with an increase in the number of moving objects that need to be monitored, localize objects in three-dimensional space, and also be able to work with heterogeneous sensors. This approach has both purely technical advantages and those related to the availability of microelectronics components in modern geopolitical realities.

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References

Yaloveha, V., Hlavcheva, D. and Podorozhniak, A. (2019), “Usage of convolutional neural network for multispectral image processing applied to the problem of detecting fire hazardous forest areas”, Сучасні інформаційні системи, Vol. 3, No 1, pp. 116–120, DOI: https://doi.org/10.20998/2522-9052.2019.1.19

Kuchuk, H., Kovalenko, A., Ibrahim, B.F. and Ruban, I. (2019), “Adaptive compression method for video information”, International Journal of Advanced Trends in Computer Science and Engineering, 8(1), pp. 66-69, DOI: http://dx.doi.org/10.30534/ijatcse/2019/1181.22019

Datsenko, S., and Kuchuk, H. (2023), “Biometric authentication utilizing convolutional neural networks”, Advanced Information Systems, vol. 7, no. 2, pp. 67–73. Doi: https://doi.org/10.20998/2522-9052.2023.3.10

Hlavcheva, D., Yaloveha, V., Podorozhniak, A. and Kuchuk, H. (2021), “Comparison of CNNs for Lung Biopsy Images Classification”, 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering, UKRCON 2021 – Proceedings, pp. 1–5, doi: https://doi.org/10.1109/UKRCON53503.2021.9575305

Dun B., Zakovorotnyi, O. and Kuchuk, N. (2023), “Generating currency exchange rate data based on Quant-Gan model”, Advanced Information Systems, Vol. 7, no. 2, pp. 68–74, doi: http://dx.doi.org/10.20998/2522-9052.2023.2.10

Svyrydov, A., Kuchuk, H. and Tsiapa, O. (2018), “Improving efficienty of image recognition process: Approach and case study”, Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, pp. 593-597, DOI: https://doi.org/10.1109/DESSERT.2018.8409201·

Kovalenko, A. A. (2014), “Approaches to the synthesis of the technical structure of a computer system forming the control system of an object of critical application”, Collection of scientific works of Kharkiv National University of Air Forces, No. 1 (38), pp. 116-119.

Худов В. Г., Кучук Г. А., Маковейчук О. М., Крижний А. В. (2016), “Аналіз відомих методів сегментування зображень, що отримані з бортових систем оптикоелектронного спостереження”, Системи обробки інформації, Вип. 9 (146). С. 77-80.

Khizhnyak, І. (2019), “Applied Information Technology of Thematic Segmentation of Optical-Electronic Images from On-board Systems of Remote Sensing of the Earth”, Advanced Information Systems, vol. 3, no. 2, pp. 40–46, doi: https://doi.org/10.20998/2522-9052.2019.2.07

Al-Azawi, R. J., Al-Jubouri, Q. S. and Mohammed, Y. A. (2019), “Enhanced Algorithm of Superpixel Segmentation Using Simple Linear Iterative Clustering”, IEEE 12th International Conference on Developments in eSystems Engineering (DeSE), vol. 19568614, doi: https://doi.org/10.1109/DeSE.2019.00038

Ткачов В. М., Коваленко А. А., Кучук Г. А., Ні Я. С. Метод забезпечення живучості високомобільної комп'ютерної мережі. Сучасні інформаційні системи. 2021. Том 5, № 2. С. 159-165. DOI: https://doi.org/10.20998/2522-9052.2021.2.22

Pesaresi, M. and Benediktsson, J. A. (2001), “A new approach for the morphological segmentation of high-resolution satellite imagery”, IEEE Trans. on Geoscience and Remote Sensing, vol. 39 (2), pp. 309–320, doi: https://doi.org/10.1109/36.905239

Avenash, R. and Viswanath, P. (2019), “Semantic Segmentation of Satellite Images using a Modified CNN with Hard-Swish Activation Function”, 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), pp. 413–420, doi: https://doi.org/10.5220/0007469604130420

Neupane, B., Horanont, Т. and Aryal, J. (2021), “Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis”, Remote Sensing, vol. 13(4), 808, doi: https://doi.org/10.3390/rs13040808

Long, J., Shelhamer, E. and Darrell, T. (2015), “Fully convolutional networks for semantic segmentation”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440, doi: https://doi.org/10.1109/CVPR.2015.7298965

Lopez, J., Branch, J. W. and Chen, G. (2019), “Line-based image segmentation method: a new approach to segment VHSR remote sensing images automatically”, European Journal of Remote Sensing, vol. 52 (1), pp. 613–631, doi: https://doi.org/10.1080/22797254.2019.1699449

Hassanien, E. and Emary, E. (2016), “Swarm Intelligence Principles, Advances, and Applications”, CRC Press, 220 p., doi: https://doi.org/10.1201/9781315222455

Kovalenko, A.A. and Kuchuk, G.A. (2016), “Optimal traffic control of a multiservice network based on the methods of sequential improvement of solutions”, Systems of armament and military equipment, No. 3 (47), pp. 59-63.

Khudov, H., Makoveichuk, O., Khizhnyak, I., Glukhov, S.,, Shamrai, N., Rudnichenko, S., Husak, M. and Khudov, R, (2022), “The Choice of Quality Indicator for the Image Segmentation Evaluation”, International Journal of Emerging Technology and Advanced Engineering, No. 12 (10), pp. 95–103, doi: https://doi.org/10.46338/ijetae1022_11

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Published

2024-04-30

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