Abstract
This paper studies moving object detection in satellite videos, which plays a significant role for large-scale video monitoring and dynamic analysis. Due to the tiny targets, complex background, and completely or partly occlusion, moving object detection accurately from each image frame is difficult and challenging. In order to tackle the issues, we propose an improved Visual Background Extractor algorithm (ViBe) with an improved Canny edge detection operator. First, the improved Canny operator based on iteration method is employed to find the optimal high and low thresholds and extract more edges information of satellite videos. Furthermore, the eight pixels are replaced with twenty-four pixels and the times of target threshold is introduced in ViBe detection to increase the selection range of backgrounds. The experiments are tested in SATSOT datasets, where the results verify that the proposed method is more robust in complex background with interference, turning and rapid motion.
Similar content being viewed by others
Data availability
The data used to support the findings of the study are available from the corresponding author upon request.
References
Zhang, J., Jia, X., Hu, J.: Error bounded foreground and background modeling for moving object detection in satellite videos. IEEE Trans. Geosci. Remote Sens. 58(4), 2659–2669 (2019)
Yang, T., Wang, X., Yao, B., Li, J., Zhang, Y., He, Z., Duan, W.: Small moving vehicle detection in a satellite video of an urban area. Sensors 16(9), 1528 (2016)
Shu, M., Zhong, Y., Lv, P.: Small moving vehicle detection via local enhancement fusion for satellite video. Int. J. Remote Sens. 19(20), 7189–7214 (2021)
Angelo, D.P., Mattyus, G., Reinartz, P.: Skybox image and video product evaluation. Int. J. Image Data Fusion 7, 3–18 (2016)
Ammar, H., Wang, X., Saleem, K., et al.: GLT: grouping-based location tracking for object tracking sensor networks. Wirel. Commun. Mob. Comput. 2017, 1–19 (2017)
Zhang, J., Jia, X., Hu, J.: Local region proposing for frame-based vehicle detection in satellite videos. Remote Sens. 11(20), 2372 (2019)
Tang, Z.: Intelligent target detection and tracking algorithm for martial arts applications. Wirel. Commun. Mob. Comput. 2022, 1–10 (2022)
Pei, W., Lu, X.: Moving object tracking in satellite videos by kernelized correlation filter based on color-name features and Kalman prediction. Wirel. Commun. Mob. Comput. 2022, 1–16 (2022)
Li, X., Zhang, L., You, J.: Domain transfer learning for hyperspectral image super-resolution. Remote Sens. 11(6), 694 (2019)
Mo, L., Guo, S.: Consensus of linear multi-agent systems with persistent disturbances via distributed output feedback. J. Syst. Sci. Complex. 32, 835–845 (2019)
Pei, W.: Staring imaging attitude tracking control laws for video satellites based on image information by hyperbolic tangent fuzzy sliding mode control. Comput. Intell. Neurosci. 2022, 1–13 (2022)
Nunziata, F., Li, X., Marino, A., Shao, W., Portabella, M., Yang, X., Buono, A.: Microwave satellite measurements for coastal area and extreme weather monitoring. Remote Sens. 13(16), 3126 (2021)
Hawkins, A., Carrico, J., Motiwala, S., Maclachlan, C.: Flight dynamics operations and collision avoidance for the skysat imaging constellation, In: 9th international workshop on satellite constellations and formation flying (IWSCFF). University of Colorado, Boulder, CO, USA (2017)
Murthy, A.K., Shearn, M., Smiley B.D.: SkySat-1: very high-resolution imagery from a small satellite. In: Proceedings of the Sensors, Systems, and Next-Generation Satellites XVIII, Amsterdam, Netherlands, September (2014)
Steckling, M., Renner, U., Röser, H.P.: DLR-TUBSAT, qualifification of high precision attitude control in orbit. Acta Astronaut. 39, 951–960 (1999)
Chen, N.: Jilin-1: China’s first commercial remote sensing satellites aim to fill the void, Chinese Academy of Sciences (2016)
Xiao, A., Wang, Z., Wang, L., Ren, Y.: Super-resolution for “Jilin-1” satellite video imagery via a convolutional network. Sensors 18(4), 1194 (2018)
Zhang, X., Xiang, J., Zhang, Y.: Space object detection in video satellite images using motion information. Int. J. Aerosp. Eng. 3, 1–9 (2017)
Wang, W., Li, Q., Tang, L.: Algorithm of vehicle detection in low altitude aerial video. J. Wuhan Univ. Technol. 32, 155–158 (2010)
Luo, Y., Liang, Y., Wang, Y.: Traffic flow parameter estimation from satellite video data based on optical flow. Comput. Eng. Appl. 54, 204–207 (2018)
Mo, L., Yu, Y., et al.: Distributed continuous-time optimization of second-order multiagent systems with nonconvex input constraints. IEEE Trans. Syst., Man, Cybernet. Syst. 51, 6404–6413 (2021)
Mo, L., Guo, S., Yu, Y.: Mean-square consensus of heterogeneous multi-agent systems with nonconvex constraints markovian switching topologies and delays. Neurocomputing 291, 167–174 (2018)
Gueguen, L., Hamid, R.: Large-scale damage detection using satellite imagery, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, pp. 1321–1328, MA, USA. June (2015)
Ma, H., Wang, Y.: Full information H2 control of Borel-measurable Markov jump systems with multiplicative noises. Mathematics 10(1), 37 (2021)
Hu, L.: Evaluation research on the application of GF-1 satellite for monitoring major engineering land. J. North China Inst. Sci. Technol. 12, 110–115 (2015)
Kalpana, G., Singhai, J.: Recursive-learning-based moving object detection in video with dynamic environment. Multimed. Tools Appl. 80(3), 1–12 (2021)
Yoo, J., Lee, G.C.: Moving object detection using an object motion reflection model of motion vectors. Symmetry 11(1), 34 (2019)
Hu, H., Xu, L., Zhao, H.: A spherical codebook in YUV color space for moving object detection. Sens. Lett. 10, 177–189 (2012)
Cuevas, C., Yáñez, E.M., García, N.: Labeled dataset for integral evaluation of moving object detection algorithms: LASIESTA. Comput. Vis. Image Underst. 152, 103–117 (2016)
Yeh, C.H., Lin, C.Y., Muchtar, K., et al.: Three-pronged compensation and hysteresis thresholding for moving object detection in real-time video surveillance. IEEE Trans. Ind. Electron. 64(6), 4945–4955 (2017)
Zhou, Z., Hu, F.: Object detection in nonstationary scenes based on background modeling. Comput. Eng. 34(24), 203–205 (2008)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. 2, 246–252 (1999)
Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Sig. Process. 2010, 1–24 (2010)
Elhabian, S.Y., El-Sayed, K.M., Ahmed, S.H.: Moving object detection in spatial domain using background removal techniques—state-of-art. Recent Patents Comput. Sci. 1, 32–34 (2008)
Luo, X., Wang, Y., Cai, B., Li, Z.: Moving object detection in traffic surveillance video: new MOD-AT method based on adaptive threshold. ISPRS Int. J. Geo-Inform. 10(11), 742 (2021)
Sun, T., Qi, Y., Geng, G.: Moving object detection algorithm based on frame difference and background subtraction. J. Jilin Univ. (Eng. Technol. Edit.) 4, 1325–1329 (2016)
Stauffer, C., Grimson, W.E.L.: 1999 adaptive background mixture models for real-time tracking. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 246–252 (1999)
Zhang, J., Chen, C.H.: Moving objects detection and segmentation in dynamic video backgrounds, In: 2007 IEEE Conference on Technologies for Homeland Security, pp. 64–69 (2007)
Wixson, L.: Detecting salient motion by accumulating directionally consistent flow. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 774–780 (2022)
Anshuman, A., Gupta, S., Singh, D.K.: Review of optical flow technique for moving object detection, In: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), pp. 409–413, Greater Noida, India, December (2016)
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, In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 383–387, Montreal, QC, Canada, November (2017)
Collins, R.T., Lipton, A.J., Kanade, T., et al.: A system for video surveillance and monitoring. VSAM Final Rep. 1, 1–68 (2000)
Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video, In: Proceedings Fourth IEEE Workshop on Applications of Computer Vision, pp. 8–14 (1998)
Tezcan, O., Ishwar, P., Konrad, J.: BSUV-net: a fully-convolutional neural network for background subtraction of unseen videos, In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2774–2783 (2020)
Babaee, M., Dinh, D.T., Rigoll, G.: A deep convolutional neural network for video sequence background subtraction. Pattern Recognit. 76, 635–649 (2018)
Yang, L., Li, J., Luo, Y., et al.: Deep background modeling using fully convolutional network. IEEE Trans. Intell. Transp. Syst. 19(1), 254–262 (2017)
Liu, Y., Liao, Y., Lin, C., Jia, Y., Li, Z., Yang, X.: Object tracking in satellite videos based on correlation filter with multi-feature fusion and motion trajectory compensation. Remote Sens. 14(3), 777 (2022)
Meng, L., Kerekes, J.P.: Object tracking using high resolution satellite imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5, 146–152 (2012)
Lei, L., Guo, D.: Multitarget detection and tracking method in remote sensing satellite video. Comput. Intell. Neurosci. 2021, 1–7 (2021)
Du, B., Sun, Y., Cai, S., Wu, C., Du, Q.: Object tracking in satellite videos by fusing the kernel correlation filter and the three-frame-difference algorithm. IEEE Geosci. Remote Sens. Lett. 15, 168–172 (2018)
Yang, X., Li, F., Lu, M., et al.: Moving object detection method of video satellite based on tracking correction detection. ISPRS Ann. Photogram. Remote Sens. Spat. Inform. Sci. 3, 701–707 (2020)
Yin, Q., Liu, T., Lin, Z., et al.: Moving object detection in satellite videos via spatial-temporal tensor model and weighted schatten p-norm minimization. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021)
Li, H., Man, Y.: Moving ship detection based on visual saliency for video satellite, In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1248–1250 (2016)
Lei, J., Dong, Y., Sui, H.: Tiny moving vehicle detection in satellite video with constraints of multiple prior information. Int. J. Remote Sens. 42(11), 4110–4125 (2021)
Zheng, D., Zhang, Y., Xiao, Z.: Deep learning-driven gaussian modeling and improved motion detection algorithm of the three-frame difference method. Mob. Inform. Syst. 2021, 1–7 (2021)
Kim, W.J., Hwang, S., Lee, J., Woo, S.: AIBM: accurate and instant background modeling for moving object detection. IEEE Trans. Intell. Transp. Syst. 23(7), 9021–9036 (2022)
Funding
This work was supported in part by the NSFC (62133001, 61520106010) and the National Basic Research Program of China (973 Program: 2012CB821200, 2012CB821201).
Author information
Authors and Affiliations
Contributions
Conceptualization contributed by W.P. (Wenjing Pei), Z.S. (Zhanhao Shi) and K.G. (Kai Gong); methodology contributed by W.P. and Z.S.; experiments contributed by Z.S. and W.P.; writing the original contributed by W.P. and Z.S.; revised by W.P., Z.S. and K.G. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The author(s) declare(s) that there is no conflict of interest regarding the publication of this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Pei, W., Shi, Z. & Gong, K. Moving object detection in satellite videos based on an improved ViBe algorithm. SIViP 18, 2543–2557 (2024). https://doi.org/10.1007/s11760-023-02929-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-023-02929-w