Towards image processing of reentry event

Kyselica, D.; Šilha, J.; Ďurikovič, R.; Bartková, D.; Tóth, J.

doi:10.2478/jamsi-2023-0003

Abstract

The space debris population is constantly increasing, including large spare upper stages and spacecraft. Atmospheric re-entry events caused by these artificial objects pose a threat to the ground-based population and their understanding and accurate modelling are required. On the 24th of October 2020, two cameras of the AMOS system in the Hawaiian Islands recorded the re-entry event of spare rocket’s upper stage. In the presented work, we aim to improve and automate the process of fragment identification between simultaneously acquired video recordings of this event. We are presenting the whole processing pipeline. First segmentation is performed to identify the cluster of interest and flying fragments within that cluster in all frames. The acquired information is used to create tracks of individual flying fragments – frame objects to be further associated between recordings.

References

Bay, H., Tuytelaars, T., And Van Gool, L. 2006. Surf: Speeded up robust features. In Computer Vision – ECCV 2006, A. Leonardis, H. Bischof, and A. Pinz, Eds. Springer Berlin Heidelberg, Berlin, Heidelberg, 404–417.
Search in Google Scholar Back to article
Bewley, A., Ge, Z., Ott, L., Ramos, F., And Upcroft, B. 2016. Simple online and realtime tracking. In IEEE international conference on image processing (ICIP). IEEE CS, 2001 L Street NW, Washington, 3464–3468.
Search in Google Scholar Back to article
Esa. 2022. ESA’s Annual Space Encironment Report. European Space Agency, Darmstadt, Germany.
Search in Google Scholar Back to article
Gonzalez, R. C. And Woods, R. E. 2007. Digital Image Processing (3rd Edition). Prentice-Hall, Inc. Upper Saddle River, NJ, USA ©2006.
Search in Google Scholar Back to article
Hahsler, M., Piekenbrock, M., And Doran, D. 2019. dbscan: Fast density-based clustering with r. Journal of Statistical Software 91, 1, 1–30.
Search in Google Scholar Back to article
Huang, T., Yang, G., And Tang, G. 1979. A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27, 1, 13–18.
Search in Google Scholar Back to article
Jiang, B., Sun, P., Zhang, Z., Tang, J., And Luo, B. 2021. Gamnet: Robust feature matching via graph adversarial-matching network. In Proceedings of the 29th ACM International Conference on Multimedia. MM ’21. Association for Computing Machinery, New York, NY, USA, 5419–5426.
Search in Google Scholar Back to article
Kalman, R. E. 1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering 82, 1 (03), 35–45.
Search in Google Scholar Back to article
Kanzler, R., Lips, T., Fritsche, B., Pontijas Fuentes, I., Bonetti, D., Letterio, F., De Miguel, G. V., Blanco Arnao, G., Palomo, P., Parigini, C., Et Al. 2017. Upgrade of drama’s spacecraft entry survival analysis codes. In 7th European Conference on Space Debris (ECSD). European Space Agency, Darmstadt, Germany.
Search in Google Scholar Back to article
Lowe, D. G. 1999. Object recognition from local scale-invariant features. In Proceedings of the Seventh IEEE International Conference on Computer Vision. Vol. 2. IEEE CS, 2001 L Street NW, Washington, 1150–1157 vol.2.
Search in Google Scholar Back to article
Petrosino, A. And Maddalena, L. 2014. Background model initialization for static cameras. In Background Modeling and Foreground Detection for Video Surveillance, Chapman and Hall/CRC. Chapman and Hall/CRC, 6000 Broken Sound Parkway NW, FL, 3–1–3–16.
Search in Google Scholar Back to article
Sarlin, P.-E., Detone, D., Malisiewicz, T., And Rabinovich, A. 2020. SuperGlue: Learning feature matching with graph neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, IEEE CS, 2001 L Street NW, Washington, 4948–4957.
Search in Google Scholar Back to article
Tóth, J., Kornoš, L., Zigo, P., Gajdoš, Š., Kalmančok, D., Világi, J., Šimon, J., Vereš, P., Šilha, J., Buček, M., et al. 2015. All-sky meteor orbit system amos and preliminary analysis of three unusual meteor showers. Planetary and space science 118, 102–106.
Search in Google Scholar Back to article
Walt, S. V. D., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., And Yu, T. 2014. Scikit-image: Image processing in python. PeerJ 2, e453.
Search in Google Scholar Back to article
Žilková, D., Matlovič, P., Tóth, J., Kornoš, L., Havrila, K., Baláž., M., Krajčovič, S., and Šilha, J. 2019. Applications of amos meteor all-sky detection system for space debris research. First International Orbital Debris Conference, Houston Lunar and Planetary Institute, https://www.hou.usra.edu/meetings/orbitaldebris2019/orbital2019paper/pdf/6083.pdf Art. No. 6083, 1–
Search in Google Scholar Back to article

Towards image processing of reentry event

Abstract

Paradigm

My account