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Machine Vision and Applications
Published in: Machine Vision and Applications 7/2018

02-08-2018 | Original Paper

Visual tracking of resident space objects via an RFS-based multi-Bernoulli track-before-detect method

Authors: Mohammadreza Javanmardi, Xiaojun Qi

Published in: Machine Vision and Applications | Issue 7/2018

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Abstract

In this paper, we propose a fast and reliable track-before-detect approach to simultaneously detect, track, and identify an unknown and variable number of resident space objects (RSOs) without any prior information and any explicit detection, which leads to better space domain awareness. Specifically, we use the point spread function concept to propose a separable likelihood function as the observation model in the random finite set-based multi-Bernoulli filtering framework. This framework clearly distinguishes RSOs from any counterfeit objects and detects and tracks them immediately after their respective appearance in background cluttered telescope imagery data. The extensive experimental results on the TAOS dataset demonstrate the robustness of the proposed method in detecting and tracking RSOs with the average optimal subpattern assignment localization error less than 2 pixels in image sequences with the signal to noise ratio as low as 9 dB and under the conditions of varying illumination and occlusion.
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Literature
1.
Buzzi, S., Lops, M., Venturino, L., Ferri, M.: Track-before-detect procedures in a multi-target environment. IEEE Trans. Aerosp. Electron. Syst. 44(3), 1135–1150 (2008)CrossRef
2.
Cox, I.J., Hingorani, S.L.: An efficient implementation of reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 18(2), 138–150 (1996)CrossRef
3.
Czyz, J., Ristic, B., Macq, B.: A particle filter for joint detection and tracking of color objects. Image Vis. Comput. 25(8), 1271–1281 (2007)CrossRef
4.
Flewelling, B., Sease, B.: Computer vision techniques applied to space object detect, track, id, and characterize. Technical report, Air Force Research Lab Kirtland AFB NM (2014)
5.
Fujimoto, K., Uetsuhara, M., Yanagisawa, T.: Statistical track-before-detect methods applied to faint optical observations of resident space objects. In: Proceedings of the Advanced Maui Optical and Space Surveillance Technical Conference (2015)
6.
Goodman, I.R., Mahler, R.P., Nguyen, H.T.: Mathematics of Data Fusion, vol. 37. Springer, Berlin (2013)MATH
7.
Hoseinnezhad, R., Vo, B.N., Vo, B.T.: Visual tracking in background subtracted image sequences via multi-bernoulli filtering. IEEE Trans. Signal Process. 61(2), 392–397 (2013)MathSciNetCrossRef
8.
Hoseinnezhad, R., Vo, B.N., Vo, B.T., Suter, D.: Visual tracking of numerous targets via multi-Bernoulli filtering of image data. Pattern Recognit. 45(10), 3625–3635 (2012)CrossRef
9.
Isard, M., MacCormick, J.: Bramble: a Bayesian multiple-blob tracker. In: Proceedings of the International Conference on Computer Vision (ICCV), vol. 2, pp. 34–41. IEEE (2001)
10.
Karttunen, H., Kröger, P., Oja, H., Poutanen, M., Donner, K.J.: Fundamental Astronomy. Springer, Berlin (2016)
11.
Koblick, D., Goldsmith, A., Klug, M., Mangus, P., Flewelling, B., Jah, M., Shanks, J., Piña, R., Stauch, J., Baldwin, J.: Ground optical signal processing architecture for contributing space-based SSA sensor data. Technical report, Air Force Research Lab Kirtland AFB NM (2014)
12.
Kristan, M., Perš, J., Perše, M., Kovačič, S.: Closed-world tracking of multiple interacting targets for indoor-sports applications. Comput. Vis. Image Underst. 113(5), 598–611 (2009)CrossRef
13.
Mahler, R.: Phd filters of higher order in target number. IEEE Trans. Aerosp. Electr. Syst. 43(4), 1523–1543 (2007)CrossRef
14.
Mahler, R.P.: Multitarget bayes filtering via first-order multitarget moments. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1152–1178 (2003)CrossRef
15.
Mahler, R.P.: Statistical Multisource–Multitarget Information Fusion. Artech House, Inc, Norwood (2007)MATH
16.
Mahler, R.P.: Advances in Statistical Multisource–Multitarget Information Fusion. Artech House, Norwood (2014)MATH
17.
Murphy, T.S., Holzinger, M.J., Flewelling, B.: Orbit determination for partially understood object via matched filter bank. In: AAS/AIAA Astrodynamics Specialists Meeting (2015)
18.
Nummiaro, K., Koller-Meier, E., Van Gool, L.: Object tracking with an adaptive color-based particle filter. Pattern Recognit. 2449, 353–360 (2002)MATH
19.
Okuma, K., Taleghani, A., De Freitas, N., Little, J.J., Lowe, D.G.: A boosted particle filter: multitarget detection and tracking. In: European Conference on Computer Vision, pp. 28–39. Springer (2004)
20.
Papi, F., Vo, B.N., Vo, B.T., Fantacci, C., Beard, M.: Generalized labeled multi-Bernoulli approximation of multi-object densities. IEEE Trans. Signal Process. 63(20), 5487–5497 (2015)MathSciNetCrossRef
21.
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: European Conference on Computer Vision, pp. 661–675. Springer (2002)
22.
Reid, D.: An algorithm for tracking multiple targets. IEEE Trans. Autom. Control 24(6), 843–854 (1979)CrossRef
23.
Reuter, S., Vo, B.T., Vo, B.N., Dietmayer, K.: The labeled multi-Bernoulli filter. IEEE Trans. Signal Process. 62(12), 3246–3260 (2014)MathSciNetCrossRef
24.
Schildknecht, T., Ploner, M., Hugentobler, U.: The search for debris in GEO. Adv. Space Res. 28(9), 1291–1299 (2001)CrossRef
25.
Schuhmacher, D., Vo, B.T., Vo, B.N.: A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Signal Process. 56(8), 3447–3457 (2008)MathSciNetCrossRef
26.
Uetsuhara, M., Ikoma, N.: Faint debris detection by particle based track-before-detect method. In: Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference (2014)
27.
Vermaak, J., Maskell, S., Briers, M., Pérez, P.: Bayesian visual tracking with existence process. In: Proceedings of the International Conference on Image Processing (ICIP), vol. 1, pp. I–721. IEEE (2005)
28.
Vo, B.N., Vo, B.T., Pham, N.T., Suter, D.: Joint detection and estimation of multiple objects from image observations. IEEE Trans. Signal Process. 58(10), 5129–5141 (2010)MathSciNetCrossRef
29.
Vo, B.T., Vo, B.N.: Labeled random finite sets and multi-object conjugate priors. IEEE Trans. Signal Process. 61(13), 3460–3475 (2013)MathSciNetCrossRef
30.
Vo, B.T., Vo, B.N., Cantoni, A.: Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Trans. Signal Process. 55(7), 3553–3567 (2007)MathSciNetCrossRef
31.
Vo, B.T., Vo, B.N., Cantoni, A.: The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Trans. Signal Process. 57(2), 409–423 (2009)MathSciNetCrossRef
32.
Vo, B.T., Vo, B.N., Hoseinnezhad, R., Mahler, R.P.: Robust multi-Bernoulli filtering. IEEE J. Sel. Top. Signal Process. 7(3), 399–409 (2013)CrossRef
33.
Yanagisawa, T., Kurosaki, H., Oda, H., Tagawa, M.: Ground-based optical observation system for LEO objects. Adv. Space Res. 56(3), 414–420 (2015)CrossRef
34.
Yanagisawa, T., Nakajima, A., Kimura, T.: The stacking method for detection of small GEO debris and moving objects. Proc. Int. Symp. Space Technol. Sci. 23, 2324–2329 (2002)
35.
Yang, M., Yu, T., Wu, Y.: Game-theoretic multiple target tracking. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 1–8. IEEE (2007)
Metadata
Title
Visual tracking of resident space objects via an RFS-based multi-Bernoulli track-before-detect method
Authors
Mohammadreza Javanmardi
Xiaojun Qi
Publication date
02-08-2018
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 7/2018
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-018-0963-6

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