HYBRID ALGORITHM OF AUTOMATIC TRACKING FOR EMBEDDED COMPUTERS OF OPTOELECTRONIC NAVIGATION AND GUIDANCE SYSTEMS
Abstract
The authors of the work carried out research in the field of technical vision systems, as well
as approaches to solving problems of detecting and tracking objects of interest without a priori
knowledge of their type, taking into account the target platform in the form of an embedded optoelectronic
system computer. Based on the data obtained, the sphere was analyzed and a new hybrid
maintenance algorithm for embedded systems was proposed. It is based on a combination of several
types of maintenance algorithms, with one of them as a priority, providing the main work, and
several auxiliary ones to stabilize and expand the functionality of the priority one. They are connected
by an external processing cycle, which, based on a consensus decision of internal algorithms,
independently decides on the position of the target object in the frame and stores auxiliary
information to ensure the correct operation of the entire algorithm, as well as responsible for making
a decision on the re-detection of the target. The authors propose two possible implementations
of this approach, used depending on the power of available computing resources. A variant of the
algorithm has been implemented for the available computing power, and its semi-natural tests
have been carried out based on real video sequences. They represent different backgrounds and
different structural objects of interest with different dynamics of change over time. The evaluation
of the results of the proposed algorithm in the tasks of detecting and tracking an object of interest
in real time on the presented videos using a software package for automating testing of detection
and tracking algorithms has been carried out. As a result, the algorithm showed high efficiency in
the tasks set, improving the accuracy of tracking, in comparison with internal algorithms that
worked separately, by adding rotary and scale invariances, and also significantly increased the
ability to re-detect an object after its loss. In conclusion, the authors present proposals for the
further development and implementation of optoelectronic systems into embedded computers
References
with YOLOv8, ArXiv, 2023. DOI: 10.48550/arXiv.2305.09972.
2. Wang, Chien-Yao & Bochkovskiy, Alexey & Liao, Hong-yuan. YOLOv7: Trainable bag-offreebies
sets new state-of-the-art for real-time object detectors, arXiv, 2022. DOI:
10.48550/arXiv.2207.02696.
3. Bertinetto L., Valmadre J., Henriques J.F., Vedaldi A., Torr P.H.S. Fully-Convolutional Siamese
Networks for Object Tracking. In: Hua G., Jégou H. (eds), Computer Vision – ECCV 2016 Workshops.
ECCV 2016. Lecture Notes in Computer Science, 2016, Vol. 9914. Springer, Cham.
4. Zhang Y., Wang L., Qi J., Wang D., Feng M., Lu H. Structured Siamese Network for Real-Time
Visual Tracking. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds), Computer Vision –
ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, 2018, Vol. 11213. Springer, Cham.
5. Li D., Yu Y. & Chen X. Object tracking framework with Siamese network and re-detection
mechanism, J Wireless Com Network, 2019, 261.
6. Fei Chen, Xiaodong Wang, Yunxiang Zhao, Shaohe Lv, Xin Niu. Visual object tracking: A survey,
Computer Vision and Image Understanding, 2022, 222. DOI: 10.1016/j.cviu.2022.103508.
7. Zahra Soleimanitaleb, Mohammad Ali Keyvanrad. Single Object Tracking: A Survey of
Methods, Datasets, and Evaluation Metrics, ArXiv, 2022. DOI: 10.48550/arXiv.2201.13066.
8. Nebehay G. and Pflugfelder R. Consensus-based matching and tracking of keypoints for object
tracking, IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs,
CO, USA, 2014, pp. 862-869. DOI: 10.1109/WACV.2014.6836013.
9. Qi Y., Zhang S., Qin L., Yao H., Huang Q., Lim J., and Yang M.H. Hedged deep tracking,
In CVPR. IEEE, 2016, pp. 4303-4311.
10. Wang N. and Yeung D.-Y. Ensemble-Based Tracking: Aggregating Crowdsourced Structured
Time Series Data, In ICML, 2014.
11. Kalal Z., Mikolajczyk K., Matas J. Tracking-Learning-Detection, IEEE Transactions on Pattern
Analysis and Machine Intelligence, 2012, Vol. 34, No. 7, pp. 1409-1422.
12. Bertinetto L., Valmadre J., Golodetz S., Miksik O. and Torr P.H.S. Staple: Complementary Learners
for Real-Time Tracking, 2016 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), Las Vegas, NV, USA, 2016, pp. 1401-1409. DOI: 10.1109/CVPR.2016.156.
13. Henriques J.F., et al. High-Speed Tracking with Kernelized Correlation Filters, IEEE Transactions
on Pattern Analysis and Machine Intelligence, 2015, Vol. 37, No. 3, pp. 583-596.
14. Tullsen D.M., Eggers S.J. and Levy H.M. Simultaneous multithreading: Maximizing on-chip
parallelism, Proceedings 22nd Annual International Symposium on Computer Architecture,
Santa Margherita Ligure, Italy, 1995, pp. 392-403.
15. Bolme David, Beveridge J., Draper Bruce, Lui Yui. Visual object tracking using adaptive correlation
filters, Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition, 2010, pp. 2544-2550. DOI: 10.1109/CVPR.2010.5539960.
16. Bondarenko V.A., El'tsova D.K., Lizin A.I., Pavlova V.A., Sozinova M.V., Tupikov V.A.
Mnogoagentnyy algoritm avtomaticheskogo obnaruzheniya i soprovozhdeniya
nedeterminirovannykh ob"ektov [Multi-agent algorithm for automatic detection and tracking
of non-deterministic objects], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering
Sciences], 2020, No. 1 (211), pp. 218-232.
17. Bondarenko V.A., Gagarina A.Yu., Pavlova V.A., Tupikov V.A. Programmnyy kompleks
avtomatizatsii testirovaniya algoritmov obnaruzheniya i soprovozhdeniya ob"ektov na
videoposledovatel'nostyakh [Software package for testing algorithms for detecting and tracking
objects on video sequences], Perspektivnye sistemy i zadachi upravleniya: Mater. XVI
Vserossiyskoy nauchno-prakticheskoy konferentsii i XII molodezhnoy shkoly-seminara
«Upravlenie i obrabotka informatsii v tekhnicheskikh sistemakh» [Promising management systems
and tasks: materials of the XVI All-Russian Scientific and Practical Conference and the
XII youth school seminar "Information Management and processing in technical systems"].
Taganrog: IP Maruk M.R., 2021, 355 p.
18. Gagarina A.Yu., Gessen P.A., Lizin A.I., Pavlova V.A., Sozinova M.V., Tupikov V.A. Kvadratnoobuchaemyy
algoritm avtomaticheskogo obnaruzheniya i soprovozhdeniya nede-terminirovannykh
ob"ektov [A square-trainable algorithm for automatic detection and tracking of non-deterministic
objects], Izvestiya Tul'skogo gosudarstvennogo universiteta. Tekhnicheskie nauki [Proceedings of
Tula State University. Technical Sciences], 2022, 11, pp. 3-13.
19. Rezatofighi H., Tsoi N., Gwak J., Sadeghian A., Reid I. Savarese S., Generalized Intersection Over
Union: A Metric and a Loss for Bounding Box Regression, 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 658-666.
20. Hodson T.O. Root-mean-square error (RMSE) or mean absolute error (MAE): when to use
them or not, Geoscientific Model Development, 2022, 15, pp. 5481-5487. DOI: 10.5194/gmd-
15-5481-2022.
