Kalman Filter for a Particular Class of Dynamic Object Images
Keywords:
dynamic object, state estimation, Kalman filter, four-point imageAbstract
We discuss the problem of estimating the state of a dynamic object by using observed images generated by an optical system. The work aims to implement a novel approach that would ensure improved accuracy of dynamic object tracking using a sequence of images. We utilize a vector model that describes the object image as a limited number of vertexes (reference points). Upon imaging, the object of interest is assumed to be retained at the center of each frame, so that the motion parameters can be considered as projections onto the axes of a coordinate system matched with the camera's optical axis. The novelty of the approach is that the observed parameters (the distance along the optical axis and angular attitude) of the object are calculated using the coordinates of specified points in the object images. For estimating the object condition, a Kalman-Bucy filter is constructed on the assumption that the dynamic object motion is described by a set of equations for the translational motion of the center of mass along the optical axis and variations in the angular attitude relative to the image plane. The efficiency of the proposed method is illustrated by an example of estimating the object's angular attitude.
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3. Biemond J., Riesek J., Gerbrands J. A fast Kalman filter for images degraded by both blur and noise. IEEE Transactions on Acoustics, Speech, and Signal Processing. 1983. vol. 31. no. 5. pp. 1248–1256.
4. Xie X., Sudhakar R., Zhuang H. Real-time eye feature tracking from a video image sequence using Kalman Filter. IEEE Transactions on Systems, Man, and Cybernetics 1995. vol. 25. no. 12. pp. 1568–1577.
5. Lippiello V., Siciliano B., Villani L. A new method of image features pre-selection for real-time pose estimation based on Kalman filter. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 2002. vol. 1. pp. 372–377.
6. Gorbachev V.A., Kalugin V.F. [Development of a multi-object tracking algorithm with untrained features of object matching]. Computer Optics – Komp'juternaja optika. 2023. vol. 47. no. 6. pp. 1002–1010. DOI: 10.18287/2412-6179-CO-1275. (In Russ.).
7. Cao K., Li J., Song R., Li Y. HELM-AD: Hierarchical and efficient attitude determination framework with adaptive error compensation module based on ELM network. ISPRS Journal of Photogrammetry and Remote Sensing. 2023. vol. 195. pp. 418–431. DOI: 10.1016/j.isprsjprs.2022.12.010.
8. Al Bitar N., Gavrilov A. A new method for compensating the errors of integrated navigation systems using artificial neural networks. Measurement. 2021. vol. 168. no. 108391.
9. Chen C., Xiong R., Yang R., Shen W., Sun F. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter. Journal of Cleaner Production. 2019. vol. 234. pp. 1153–1164. DOI: 10.1016/j.jclepro.2019.06.273.
10. Cui Z., Kang L., Li L., Wang L., Wang K. A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF. Energy. 2022. vol. 259. no. 124933.
11. Astafiev A.V., Titov D.V., Zhiznyakov A.L., Demidov A.A. A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural networks. Computer Optics – Komp'juternaja optika. 2021. vol. 45. no. 2. pp. 277–285. DOI: 10.18287/2412-6179-CO-826. (In Russ.).
12. Baoa T., Zhao Y., Zaidia S.A.R., Xiea S., Yang P., Zhang Z.. A deep Kalman filter network for hand kinematics estimation using sEMG. Pattern Recognition Letters. 2021. vol. 143. pp. 88–94. DOI: 10.1016/j.patrec.2021.01.001.
13. Chen K., Yu J. Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach. Applied Energy. 2014. vol. 113. pp. 690–705. DOI: 10.1016/j.apenergy.2013.08.025.
14. Din F. Combined state and least squares parameter estimation algorithms for dynamic systems. Applied Mathematical Modelling. 2014. vol. 38. no. 1. pp. 403–412. DOI: 10.1016/j.apm.2013.06.007.
15. Ding F. State filtering and parameter estimation for state space systems with scarce measurements. Signal Processing. 2014. vol. 104. pp. 369–380. DOI: 10.1016/j.sigpro.2014.03.031.
16. Zhu X.-Z., Cabecinhas D., Xie W., Casau P., Silvestre C., Batista P., Oliveira P. Kalman–Bucy filter-based tracking controller design and experimental validations for a quadcopter with parametric uncertainties and disturbances. International Journal of Systems Science. 2023. vol. 54(1). pp. 17–41. DOI: 10.1080/00207721.2022.2096939.
17. Ionov I., Boldyrikhin N., Cherckesova L., Saveliev V. Filtering grayscale images using the Kalman filter. E3S Web of Conferences. 2022. vol. 363. DOI: 10.1051/e3sconf/202236303004.
18. Jwo D.-J., Biswal A. Implementation and Performance Analysis of Kalman Filters with Consistency Validation. Mathematics. 2023. vol. 11(3). no. 521. DOI: 10.3390/math11030521.
19. Piovoso M., Laplante P.A. Kalman filter recipes for real-time image processing. Real-Time Imaging. 2003. vol. 9. no. 6. pp. 433–439. DOI: 10.1016/j.rti.2003.09.005.
20. Vasilyuk N.N. Correction of rotational blur in images of stars observed by an astroinertial attitude sensor against the background of the daytime sky. Computer Optics – Komp'juternaja optika. 2023. vol. 47. no. 1. pp. 79–91. DOI: 10.18287/2412-6179-CO-1141. (In Russ.).
Published
2024-06-26
How to Cite
Soifer, V., Fursov, V., & Kharitonov, S. (2024). Kalman Filter for a Particular Class of Dynamic Object Images. Informatics and Automation, 23(4), 953-968. https://doi.org/10.15622/ia.23.4.1
Section
Artificial Intelligence, Knowledge and Data Engineering
Copyright (c) Владимир Алексеевич Фурсов, Виктор Александрович Сойфер, Сергей Иванович Харитонов
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