Journal of Radio Electronics. eISSN 1684-1719. 2023. 11
Contents

Full text in Russian (pdf)

Russian page

 

DOI: https://doi.org/10.30898/1684-1719.2023.11.9

 

SEGMENTATION ALGORITHM FOR SELECTING MOVING OBJECTS

BASED ON BACKGROUND RECONSTRUCTION

USING NOISE CHARACTERISTICS PARAMETERS

 

V.P. Fedosov, R.R. Ibadov, S.R. Ibadov

 

Institute of Radio Engineering Systems and Control, Southern Federal University

347928, Russia, Taganrog, Nekrasovsky per., 44.

 

The paper was received September 5, 2023.

 

Abstract. One of the most important tasks for ensuring life safety is monitoring objects and tracking the behavior of people in urban infrastructure from an unmanned aerial vehicle (UAVs). Difficulties arise when the UAVs is used for surveillance in areas that are difficult or unsuitable for humans to access, and then there is a risk of accidents. Therefore, analysis, reconstruction and tracking of objects can help in solving existing cases. As a critical technology in computer vision and video processing, video object segmentation has far-reaching pragmatic and application implications. In this paper, we propose an algorithm for segmenting video objects based on background reconstruction for extracting moving objects from a video sequence taken from a UAVs. In the theoretical part of the work, methods for detecting and segmenting moving objects are considered based on background subtraction algorithms, the optical flow of the Kalman filter. An exact mask for detecting changes is proposed, taking into account the estimate of the threshold of noise characteristics, as well as an algorithm for detecting movements. A qualitative analysis of the background reconstruction error was carried out using a statistical criterion. A modified algorithm for obtaining a change detection mask (MDM) using a threshold estimated using noise characteristics is proposed. The results of segmentation of objects, calculation of the root-mean-square error of background reconstruction for the proposed method when extracting moving objects are shown. The developed method allows you to effectively recognize and extract moving objects in a video sequence. This algorithm is suitable for the case when the UAVs freezes in the air. After the background reconstruction, there are small defects that can be eliminated using morphological operations.

Key words: segmentation, image reconstruction, correlation, dispersion, UAVs.

Financing: The study was carried out with the support of a grant from the Russian Science Foundation No. 22-29-01389 of 21.12.2021 at the Southern Federal University.

Corresponding author: Ibadov Ragim Raufevich, ragim_ibadov@mail.ru

 

References

1. Wang Q., Rao Y. Visual Analysis of Human Motion: A Survey on Recent Advances and Applications. IEEE Visual Communications and Image Processing (VCIP), 2018. Pp. 1-4. doi: 10.1109/VCIP.2018.8698618.

2. Zhang, Y., Yang, S., Li, H., Xu, Z. Shadow tracking of moving target based on CNN for video SAR system. IEEE International Geoscience and Remote Sensing Symposium. 2018. Pp. 4399-4402. doi: 10.1109/IGARSS.2018.8518431.

3. Pavithra G., Jose J. J., Chandrappa T. A. Real-time color classification of objects from video streams. IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2017. Pp. 1683-1686. doi: 10.1109/RTEICT.2017.8256886.

4. ShangGuan H., Mukundan R. Video based motion capture in environments with non-stationary background. IEEE International Conference on Signals and Systems (ICSigSys), 2017. Pp. 44-49. doi: 10.1109/ICSIGSYS.2017.7967067.

5. Makino, K., Shibata, T., Yachida, S., Ogawa, T., Takahashi, K. Moving-object detection method for moving cameras by merging background subtraction and optical flow methods. IEEE Global Conference on Signal and Information Processing, 2017. Pp.383-387. doi: 10.1109/GlobalSIP.2017.8308669

6. Yu, T., Yang, J., Lu, W. Dynamic background subtraction using histograms based on fuzzy c-means clustering and fuzzy nearness degree. IEEE Access. 2019. Vol. 7. Pp. 14671-14679. doi: 10.1109/ACCESS.2019.2893771.

7. Akilan, T., Wu, Q. J., Yang, Y. Fusion-based foreground enhancement for background subtraction using multivariate multi-model Gaussian distribution. Information Sciences, 2018. Vol. 430. Pp. 414-431. https://doi.org/10.1016/j.ins.2017.11.062

8. Yao, R., Lin, G., Xia, S., Zhao, J., Zhou, Y. Video object segmentation and tracking: A survey. ACM Transactions on Intelligent Systems and Technology (TIST), 2020. Vol. 4. Pp. 1-47. https://doi.org/10.1145/3391743

9. Liu, C., Wang, W., Shen, J., Shao, L. Stereo video object segmentation using stereoscopic foreground trajectories. IEEE transactions on cybernetics, 2018. Vol.49. No 10. Pp. 3665-3676. doi: 10.1109/TCYB.2018.2846361

10. Yang, C., Lamdouar, H., Lu, E., Zisserman, A., Xie, W. Self-supervised video object segmentation by motion grouping. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. Pp. 7177-7188. https://doi.org/10.48550/arXiv.2104.07658

11. Zhuo, T., Cheng, Z., Zhang, P., Wong, Y., Kankanhalli, M. Unsupervised online video object segmentation with motion property understanding. IEEE Transactions on Image Processing, 2019. Vol. 29. Pp. 237-249. doi: 10.1109/TIP.2019.2930152

12. Aung S. S., Kyu Z. M. Modified codebook algorithm with Kalman filter for foreground segmentation in video sequences. International Conference on Signal Processing and Communication (ICSPC), 2017. Pp. 332-336.

13. Liu, G., Wu, H. C., Xiang, W., Ye, J., Wu, Y., Pu, L. Indoor object localization and tracking using deep learning over received signal strength. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting. 2020. Pp. 1-6. doi: 10.1109/BMSB49480.2020.9379587

14. Hong-qiang B. Research on video motion object segmentation for content-based application. Journal of Shanghai University (English Edition), 2006. Vol. 2. no. 10. – Pp. 142-143.

15. Fedosov, V. P., Ibadov, S. R., Ibadov, R. R., Kucheryavenko, S. V. Method For Detecting Violation at a Pedestrian Crossing Using a Convolutional Neuaral Network. IEEE Radiation and Scattering of Electromagnetic Waves (RSEMW), 2021. Pp. 451-454. doi: 10.1109/RSEMW52378.2021.9494089.

16. Fedosov V. P., Ibadov R. R., Ibadov S. R. Restoration of the Lost Area of the Underlying Surface Image Using the Saliency Map. IEEE Radiation and Scattering of Electromagnetic Waves (RSEMW), 2021. Pp. 447-450. doi: 10.1109/RSEMW52378.2021.9494112.

17. Ibadov R. R., Ibadov, R. R., Gapon, N. V., Ibadov, S. R., Kucheryavenko, S. V. Image reconstruction using the modified texture synthesis algorithm IOP Conference Series: Materials Science and Engineering, 2021. Vol. 1029. no. 1. Pp. 012117. doi: 10.1088/1757-899X/1029/1/012117.

For citation:

Fedosov V.P., Ibadov R.R., Ibadov S.R. Segmentation algorithm for selecting moving objects based on background reconstruction using noise characteristics parameters. // Journal of Radio Electronics. – 2023. – №. 11. https://doi.org/10.30898/1684-1719.2023.11.9 (In Russian)