Identification of the object motion trajectory in a digital image using the Kalman filter

Authors

DOI:

https://doi.org/10.24136/jaeee.2023.009

Keywords:

camera, BLOB, experiment, digital image, Matlab

Abstract

The paper concerns identification of the object motion trajectories in a digital image. An image analysis procedure was used consisting of two methods: BLOB identification and Kalman filter. BLOB identification is the superior method, and the Kalman filter is an alternative one, used to determine the motion trajectories only in the event of incorrect BLOB identification. Data from the correct BLOB identification are used on an ongoing basis in the process of training the Kalman filter. In this way, the filter becomes a model of a physical object that allows predicting the future state based on the object's current behaviour. The use of two complementary image analysis methods allows to determine the motion trajectories of the object's characteristic points in conditions of uneven scene lighting. The effectiveness of the method was confirmed in experimental rests.

References

Dalka P., Metody algorytmicznej analizy obrazu wizyjnego do zastosowań w monitorowaniu ruchu drogowego, Praca doktorska, Politechnika Gdańska, 2014.

Galda, H., Development of a segmentation method for dermoscopic images based on color clustering, Kobe University, August, 2003.

Gonzales R. C., Woods, R.E. and Eddins, S.L., Digital Image Processing Using MATLAB, Gatesmark Publishing, 2020.

Goshtasby A., Image registration by local approximation meth-ods,"Image and Vision Computing, 6, 1988, 255-261.

Goshtasby A., Piecewise linear mapping functions for image registration," Pattern Recognition, 19, 1986, 459-466.

Guilluy W., Oudre L., Beghdadi A. (2021) “Video stabilization: Overview, challenges and perspectives”. Signal Processing: Image Communication, 90.

Jia, T., Sun N., Cao M., Moving object detection based on blob analysis. IEEE International Conference on Automation ad Logistics, Qingadao, China, 2008. 322-325.

Lee T.H., Lee Y., Song B. C. (2014) “Fast 3D video stabilization using ROI-based warping”. Journal of Visual Communication and Image Representation, 25, 943-950.

Moeslund T. B., Introduction to video and image processing: building real systems and applications. Undergraduate Topics in Computer Science book series, Aalborg, Dania, 2012.

Noureldin A., Karamat T., Georgy J., Fundamentals of Inertial Navi-gation, Satellite-based Positioning and their Integration, Springer, 2013.

Pavlovic M., Banjac Z., Kovacevic B. (2022) “Digital Video Stabilization Verification Based on Genetic Algorithm Template Matching”. Advances in Electrical and Computer Engineering, 22, 53-60.

Piatkowski T., Wolski M., Osowski P., Method for determining motion trajectories of characteristic points registered by a video camera, Engineering Mechanics, 24rd International Conference Engineering Mechanics, Svratka, Czech Republic, 14-17.05.2018, 677-680.

Piatkowski T., Wolski M., Tomaszewski T., Strzelecki P., Sempruch J., Analysis of the positioning process of objects on an oblique plane with barrier, Mechanism and Machine Theory, 2021.

Piątkowski T., Zagadnienie skalowania obrazu z kamery cyfrowej w badaniach eksperymentalnych torów ruchu sortowanych obiektów, Współczesne wyzwania transportu i elektrotechniki: monografia pod red. Tomasza Ciszewskiego i Jerzego Wojciechowskiego, UTH Radom, 2021.

Ren Z., Chen C., Fang M. (2021) Self-calibration method of gyroscope and camera in video stabilization. 2021 International Conference on Electronic Information Engineering and Computer Science, September, 23-26 2021, Changchun, China.

Rudnicki Z, Metody komputerowej analizy obrazów w badaniach tribologicznych, Wydawnictwo AGH, Kraków 2010.

Wang Y., Huang Q., Jiang C., Liu J., Shang M., Miao Z., (2023) “Video stabilization: A comprehensive survey”, Neurocomputing, 516, 205-230.

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Published

2024-02-04

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Articles