Zhurnal Radioelektroniki - Journal of Radio Electronics. eISSN 1684-1719. 2022. №11
ContentsFull text in Russian (pdf)
DOI: https://doi.org/10.30898/1684-1719.2022.11.7
DETECTION AND TRACKING OF OBJECTS
BASED ON SaLIENCY MAP FOR UAVs
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 Оctober 24, 2022.
Abstract. Object detection and tracking is one of the most important areas of computer vision, as well as applications in unmanned aerial vehicles (UAVs). However, due to the ultra-maneuverability of the UAV mobile platform, the image in the air is usually blurry and has a low signal-to-noise ratio, which is associated with the working environment. To solve the problem of detecting ground objects on the UAV platform, traditional methods of pattern recognition and classification are often ineffective. The visual attention model is a kind of bionic vision model with good stability. In the article for the detection of ground objects using UAVs, a method based on the visual saliency model is proposed, applied to a complex terrain background for object detection. To quickly isolate terrestrial features in aerial photographs, the extracted gradient function is applied to detect an area of visual significance, and then the target image is extracted using a segmentation algorithm. The subject of the study is the method and algorithm for detecting ground objects on the map of the underlying surface using the visual saliency model and object segmentation. The object of the study is a set of video sequences of terrain maps with different terrain. The novelty of the work is an algorithm that allows detecting ground targets based on an attention map using an object segmentation algorithm. A new method for segmenting objects in a video sequence is proposed. Experimental studies were carried out on the basis of video sequences of the map of the underlying surface with different backgrounds and an analysis of the results obtained was carried out. The results obtained make it possible to identify objects in the region of interest. As a result of solving the formulated tasks, the following conclusions can be drawn: An algorithm for detecting ground objects based on an saliency map has been developed. An algorithm for segmentation of ground objects based on a gradient map has been developed. An analysis of the results of the study showed that the proposed algorithm makes it possible to detect and highlight ground objects on a map of terrain with different terrain.
Key words: saliency map, object detection, image segmentation, gradient, lay, 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. Mannan S.K., Kennard C., Husain M. The role of visual salience in directing eye movements in visual object agnosia. Current biology. 2009. V.19. №6. P.247-248. https://doi.org/10.1016/j.cub.2009.02.020
2. Cheng M.M., Mitra N.J., Huang X., Torr P.H., Hu S.M. Global contrast based salient region detection. IEEE transactions on pattern analysis and machine intelligence. 2014. V.37. №3. P.569-582. https://doi.org/10.1109/tpami.2014.2345401
3. Ko B.C., Nam J.Y. Object-of-interest image segmentation based on human attention and semantic region clustering. JOSAA. 2006. V.23. №10. P.2462-2470. https://doi.org/10.1364/josaa.23.002462
4. Rutishauser U., Walther D., Koch C., Perona P. Is bottom-up attention useful for object recognition? Conference on Computer Vision and Pattern Recognition 2004. V.2. P.II-II. https://doi.org/10.1109/CVPR.2004.1315142
5. Christopoulos C., Skodras A., Ebrahimi T. The JPEG2000 still image coding system: an overview. IEEE transactions on consumer electronics. 2000. V.46. №4. P.1103-1127. https://doi.org/10.1109/30.920468
6. Zhang G.X., Cheng M.M., Hu S.M., Martin R.R. A shape‐preserving approach to image resizing. Computer Graphics Forum. 2009. V.28. №7. P.1897-1906. https://doi.org/10.1111/j.1467-8659.2009.01568.x
7. Chen T., Cheng M. M., Tan P., Shamir A., Hu S. M. Sketch2photo: Internet image montage. ACM transactions on graphics. 2009. V.28. №5. P.1-10. https://doi.org/10.1145/1618452.1618470
8. Wang T., Zhang Y., Cai Z., Wang Y., You Z. Visual attention based target detection and tracking for UAVs. IEEE Chinese Guidance, Navigation and Control Conference. 2016. P.895-900. https://doi.org/10.1109/CGNCC.2016.7828904
9. Toosy A.T., Ciccarelli O., Parker G.J., Wheeler-Kingshott C.A., Miller D.H., Thompson A.J. Characterizing function–structure relationships in the human visual system with functional MRI and diffusion tensor imaging. Neuroimage. 2004. V.21. №4. P.1452-1463. https://doi.org/10.1016/j.neuroimage.2003.11.022
10. Gozli D.G., Moskowitz J.B., Pratt J. Visual attention to features by associative learning. Cognition. 2014. V.133. №2. P.488-501. https://doi.org/10.1016/j.cognition.2014.07.014
11. Itti L., Koch C., Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on pattern analysis and machine intelligence. 1998. V. 20. №11. P.1254-1259. https://doi.org/10.1109/34.730558
12. Gao D., Vasconcelos N. Decision-theoretic saliency: computational principles, biological plausibility, and implications for neurophysiology and psychophysics. Neural computation. 2009. V.21. №1. P.239-271. https://doi.org/10.1162/neco.2009.11-06-391
13. Hou X., Zhang L. Saliency detection: A spectral residual approach. IEEE Conference on computer vision and pattern recognition. 2007. P.1-8. https://doi.org/10.1109/CVPR.2007.383267
14. Wan-Yi L., Peng W., Hong Q. A survey of visual attention based methods for object tracking. Acta Automatica Sinica. 2014. V. 40. №.4. P.561-576.
15. 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. 2021. P.447-450. https://doi.org/10.1109/RSEMW52378.2021.9494112
16. Fedosov V.P., Ibadov R.R., Ibadov S.R., Voronin V.V. Restoration of the Blind Zone of the Image of the Underlying Surface for Radar Systems with Doppler Beam Sharpening. IEEE Radiation and Scattering of Electromagnetic Waves. 2019. P.424-427. http://sci-hub.cc/10.1109/RSEMW.2019.8792685
17. Kurita T., Otsu N., Abdelmalek N. Maximum likelihood thresholding based on population mixture models. Pattern recognition. 1992. V.25. №10. P.1231-1240. https://doi.org/10.1016/0031-3203(92)90024-D
For citation:
Fedosov V.P., Ibadov R.R., Ibadov S.R. Detection and tracking of objects based on saliency map for UAV. Zhurnal radioelektroniki [Journal of Radio Electronics] [online]. 2022. №11. https://doi.org/10.30898/1684-1719.2022.11.7 (In Russian)