A hierarchical list system for detecting and tracking point weakly emitting objects by image sequence

For Russian citation (Opticheskii Zhurnal):

Меденников П.А., Павлов Н.И. Иерархическая система списков для обнаружения и сопровождения точечных слабоизлучающих объектов по последовательности изображений // Оптический журнал. 2024. Т. 91. № 11. С. 12–23. http://doi.org/10.17586/1023-5086-2024-91-11-12-23

Medennikov P.A., Pavlov N.I. A hierarchical list system for detecting and tracking point weakly emitting objects by image sequence [in Russian] // Opticheskii Zhurnal. 2024. V. 91. № 11. P. 12–23. http://doi.org/10.17586/1023-5086-2024-91-11-12-23

For citation (Journal of Optical Technology):

Abstract:

Subject of study. The problem of detection and tracking of point weakly emitting moving objects. Aim of study. Development of an image sequence processing method for stable detection and tracking of objects with low signal-to-noise ratios. Method. The main feature of the proposed image sequence processing method is the operation with a hierarchical list system: a list of targets, an initial list and a buffer list. All marks found during intra-frame image processing and which did not associate with the objects from these lists are entered in the buffer list located at the lower level of the hierarchy. If the frame mark successfully associates with the buffer list object, it is transferred to the initial list. Candidates for track initiation are gathered there, and are transferred to the list of targets upon confirmation of the corresponding condition. Various rules are used for association of frame marks with objects in these lists, rules for recalculating track and energy characteristics, as well as various conditions for holding an object in the list. Main results. A method is proposed that allows objects with a low probability of detection on a single frame to be detected and tracked. The efficiency of the proposed method is confirmed by the conducted statistical experiments. Practical significance. Application of the developed method will increase the detection range and stability of tracking poorly visible objects in real-time detection systems.

Keywords:

point target detection, target tracking, images sequence processing, track maintenance, data association

OCIS codes: 100.4999

References:

1. Меденников П.А., Павлов Н.И. Обнаружение и сопровождение точечных слабоизлучающих объектов на основе анализа последовательности минисерий изображений // Оптический журнал. 2021. Т. 88. № 12. С. 50–58. https://doi.org/10.17586/1023-5086-2021-88-12-50-58
Medennikov P.A., Pavlov N.I. Detection and tracking of weakly emitting point objects, based on the analysis of a sequence of miniseries of images // J. Opt. Technol. 2021. V. 88. № 12. P. 716–721. https://doi.org/10.1364/JOT.88.000716
2. Bar-Shalom Y., Li X.-R. Multitarget-multisensor tracking: Principles and techniques. Storrs: YBS Publ., 1995. 615 p.
3. Blackman S.S., Popoli R. Design and analysis of modern tracking systems. Boston: Artech House, 1999. 1232 p.
4. Коновалов А.А. Основы траекторной обработки радиолокационной информации. Ч. 1. СПб.: изд. СПбГЭТУ «ЛЭТИ», 2013. 164 с.
Konovalov A.A. Base of tracking processing of radiolocation data. P. 1 [in Russian]. St. Petersburg: St. Petersburg GETU «LETI» Publ., 2013. 164 p.
5. Коновалов А.А. Основы траекторной обработки радиолокационной информации. Ч. 2. СПб.: изд. СПбГЭТУ «ЛЭТИ», 2014. 180 с.
Konovalov A.A. Base of tracking processing of radiolocation data. P. 4 [in Russian]. St. Petersburg: St. Petersburg GETU "LETI" Publ., 2014. 180 p.
6. Фисенко В.Т., Можейко В.И., Фисенко Т.Ю. и др. Метод автоматического обнаружения и прослеживания многих малоразмерных объектов в условиях априорной неопределенности // Изв. вузов, Приборостроение. 2014. Т. 57. № 10. С. 17–22.
Fisenko V.T., Mozhezeiko V.I., Fisenko T.Yu., et al. Method of automatically detecting and tracking many small objects under conditions of a priori indeterminacy [in Russian] // Izv. Vyssh. Uchebn. Zaved, Prib. (News of higher educational institutions, Instrument engineering). 2014. V. 57. № 10. P. 17–22.
7. Kingston R.H. Detection of optical and infrared radiation. Berlin Heidelberg: Springer-Verlag, 1978. 140 p.
8. Hadzagic M., Michalska H., and Lefebvre E. Trackbefore detect methods in tracking low-observable targets: A survey // Sensor & Transducers M. (S&T e-Digest). Special Issue. August 2005. P. 374–380.
9. Ristic B., Arulampalam S., and Gordon N. Beyond the Kalman filter: Particle filters for tracking applications. Washington DC: Artech House, 2004. 318 p.
10. Nichtern O. and Rotman S.R. Parameter adjustment for a dynamic programming track-before-detectbased target detection algorithm // J. Adv. in Sig. Process. 2008. (ID 146925). P. 1–19. https://doi.org/10.1155/2008/146925
11. Hoonkyung Cho, Joohwan Chun. A new TVD-DP algorithm using multiple IR sensors to locate the target launch point // Proc. SPIE. 2015. V. 8185. P. 8185OP-1–8185OP-14.
12. Huanhai Yang. Detection and tracking of infrared dim small image sequence moving target // The Open Automation and Control Systems J. 2015. № 7. P. 1698–1704.
13. Bendong Zhao, Shanzhu Xiao, Huanzhang Lu, et al. Point target detection in space-based infrared imaging system based on multi-direction filtering fusion // Progress in Electromagnetics Res. M. 2017. V. 56. P. 145–156.
14. Yi W., Fang Z., Li W., et al. Multi-frame trackbefore-detect algorithm for maneuvering target tracking // IEEE Trans. Vehicular Technol. 2020. V. 69. № 4. P. 4104–4118.
15. Беренков Н.Р., Тартаковский А.Г. Эффективные алгоритмы выделения слаборазличимых следов космических объектов // Труды МФТИ. 2020. Т. 12. № 2. С. 5–20.
Berenkov N.R. and Tartakovski A.G., Effective algorithms for distinguishing weakly distinguishable tracks of space objects [in Russian] // Trudy Mosk. Fiz.-Tekhnich. Inst. (Рroceedings of the Moscow Institute of Physics and Technology). 2020. V. 12. № 2.
P. 5–20.
16. Mazurek P. Convolutional neural network reference for track-before-detect-based target detection application // Remote Sens. 2023. V. 15(4629). P. 1–17. https://doi.org/10.3390/rs15184629
17. Меденников П.А. Алгоритм обнаружения и определения координат точечного объекта // Оптический журнал. 2019. Т. 86. № 8. С. 65–69. https://doi.org/10.17586/1023-5086-2019-86-08-65-69
Medennikov P.A. Algorithm for detecting and determining the coordinates of a point object // J. Opt. Technol. 2019. V. 86. № 8. P. 510–514. https://doi.org/10.1364/JOT.86.000510
18. Соболев А.А. Сравнение быстродействия методов решения задачи о назначениях при селекции в траекторной обработке // Вестник Концерна ВКО «Алмаз-Антей». 2023. № 2. С. 81–89. https://doi.org/10.38013/2542-0542-2023-2-81-89
Sobolev A.A. Performance comparison of the methods for solving the assignment problem in selection of tracking data processing [in Russian] // Vestnik Koncerna VKO "Almaz-Antey". (Bulletin of the Almaz-Antey Aerospace Defense Concern). 2023. № 2. P. 81–89. https://doi.org/10.38013/2542-0542-2023-2-81-89
19. Абакумова А.А., Малинова Т.П., Меденников П.А. и др. Программно-алгоритмический комплекс имитационного моделирования для исследования и разработки оптико-электронных систем наблюдения // Оптический журнал. 2019. Т. 86. № 8. С. 56–64. https://doi.org/10.17586/1023-5086-2019-86-08-56-64
Abakumova A.A., Malinova T.P., Medennikov P.A., et al. Algorithmic simulation-modeling software complex for the investigation and development of optoelectronic observation systems // J. Opt. Technol. 2019. V. 86. № 8. P. 503–509. https://doi.org/10.1364/JOT.86.000503