MODULE FOR ADJUSTING PARAMETERS OF ALGORITHMS FOR AUTOMATIC DETECTION AND TRACKING OF OBJECTS FOR OPTOELECTRONIC SYSTEMS
Abstract
In order to create an innovative module for automatic correction of algorithms for automatic
detection and tracking of objects with real-time training, a study of world experience in the field
of general-purpose automatic tracking with the ability to recognize the tracking object for use in
embedded computing devices of optoelectronic systems of promising robotic complexes was carried
out. Based on the conducted research, methods and approaches have been selected and tested
that allow with the greatest accuracy, while maintaining high computational efficiency, to provide
on-the-fly training of classifiers (online learning) without a priori knowledge of the type of tracking object and to ensure subsequent correction during tracking and detection of the original object
in case of its short-term loss. Such methods include a histogram of directional gradients – a descriptor
of key features based on the analysis of the distribution of brightness gradients of an object
image. Its use allows you to reduce the amount of information used without losing key data
about the object and increase the speed of image processing. The article substantiates the choice
of one of the classification algorithms in real time, which allows solving the problem of binary
classification - the method of support vectors. Due to the high speed of data processing and the
need for a small amount of initial training data to build a separating hyperplane, on the basis of
which the classification of objects takes place, this method is chosen as the most suitable for solving
the task. To implement online training, a modification of the support vector machine was chosen,
implementing stochastic gradient descent at each step of the algorithm – Pegasos. Another
auxiliary method is the clustering method of key points – this ensures an accelerated selection of
objects for classification and training. The authors of the study carried out the development and
semi-natural modeling of the proposed module, evaluated the effectiveness of its work in the tasks
of correcting and detecting the object of interest in real time with preliminary online training in
the process of tracking the object. The developed algorithm has shown high efficiency in solving
the problem. In conclusion, proposals are presented to further improve the accuracy and probability
of detecting an object of interest by the developed algorithm, as well as to improve its performance
by optimizing calculations.
References
Networks for Object Tracking, In: Hua G., Jégou H. (eds), Computer Vision – ECCV
2016 Workshops: Lecture Notes in Computer Science. Springer, Cham, 2016, Vol. 9914.
2. 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: Lecture Notes in Computer Science. Springer, Cham, 2018, Vol. 11213.
3. Li D., Yu Y. & Chen X. Object tracking framework with Siamese network and re-detection
mechanism, J Wireless Com Network, 2019, 261.
4. 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.
5. 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), 2016, P. 1401-1409. DOI: 10.1109/CVPR.2016.156.
6. Dalal N., Triggs B. Histograms of oriented gradients for human detection, 2005 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego,
CA, USA, 2005, Vol. 1, pp. 886-893. DOI: 10.1109/CVPR.2005.177.
7. Rybski P.E., Huber D., Morris D.D., Hoffman R. Visual classification of coarse vehicle orientation
using Histogram of Oriented Gradients features, 2010 IEEE Intelligent Vehicles Symposium,
La Jolla, CA, USA, 2010, pp. 921-928. DOI: 10.1109/IVS.2010.5547996.
8. Dollár P., Appel R., Belongie S. and Perona P. Fast Feature Pyramids for Object Detection,
IEEE Transactions on Pattern Analysis and Machine Intelligence, Aug. 2014, Vol. 36, No. 8,
pp. 1532-1545.
9. Samsonov N.A., Gneushev A.N. Deskriptor v akkumulyatornom prostranstve Hafa
gradientnogo polya izobrazheniya dlya detektirovaniya peshekhodov [Descriptor in the accumulator
space of the Hough gradient field of the image for detecting pedestrians], Mashinnoe
obuchenie i analiz dannykh [Machine learning and data analysis], 2017, No. 3, Vol. 3, pp. 203-
215. eISSN: 2223-3792.
10. Misra I., Shrivastava A., Hebert M. HOG and Spatial Convolution on SIMD Architecture,
Technical report. Robotics Institute, Carnegie Mellon University, 2013.
11. Huang C., Huang J. A Fast HOG Descriptor Using Lookup Table and Integral Image, ArXiv,
abs/1703.06256, 2017.
12. Roshan K., Saurabh S. Machine Learning: A Review on Binary Classification, International
Journal of Computer Applications, 2017.
13. Cortes C., Vapnik V. Support Vector Networks, Mach. Learn., 1995, Vol. 20, pp. 273-297
14. Burges J.C. A tutorial on support vector machines for pattern recognition, Data Min. Knowl.
Disc., 1998, pp. 121-167.
15. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and other kernelbased
learning methods. Cambridge University Press, Cambridge, 2000.
16. Zhou X., Zhang X., Wang B. Online Support Vector Machine: A Survey, In: Kim J., Geem Z.
(eds), Harmony Search Algorithm. Advances in Intelligent Systems and Computing, Vol. 382.
Springer, Berlin, Heidelberg, 2016.
17. Shalev-Shwartz S., Singer Y., Srebro N. et al. Pegasos: primal estimated sub-gradient solver for
SVM, Math. Program., 2020, Vol. 127, pp. 3-30.
18. Bondarenko V.A., El'cova D.K., Lizin A.I., Pavlova V.A., Sozinova M.V., Tupikov V.A.
Mnogoagentnyy algoritm avtomaticheskogo obnaruzheniya i soprovozhdeniya nedetermini
rovannykh ob"ektov [Multi-agent algorithm for automatic detection and tracking of nondeterministic
objects], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences],
2020, No. 1 (211), pp. 218-232.
19. 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 automation of algorithms for detecting
and tracking objects on video sequences], Perspektivnye sistemy i zadachi upravleniya:
Mater. XVI Vserossijskoy nauchno-prakticheskoy konferentsii i XII molodezhnoy shkolyseminara
«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.
20. 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.
21. Heikkilä M., Pietikäinen M. A texture-based method for modeling the background and detecting
moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,
Vol. 28 (4), pp. 657-662.
22. Kertész C. Texture-Based Foreground Detection, International Journal of Signal Processing,
Image Processing and Pattern Recognition (IJSIP), 2011, Vol. 4, No. 4.
23. Khalifa T. Şengül G. The Integrated Usage of LBP and HOG Transformations and Machine
Learning Algorithms for Age Range Prediction from Facial Images, Tehnicki Vjesnik, Vol. 25,
pp. 1356-1362. 10.17559/TV-20170308030459, 2018.
