• A. G. Spevakov Southwest State University, Kursk
  • S. V. Spevakova Southwest State University, Kursk
  • I. S. Matiushin Southwest State University, Kursk



Moving object detection, vision systems, object detection method, mobile systems.


Context. In the study, the task of identifying objects moving in space from a mobile system of technical vision is considered.
The analysis of the modern methods of dynamic object identification from both stationary and moving platforms is conducted. The
need to create a new method for the identification of dynamic objects with a mobile optical-electronic system, which is adaptive to
changing observation conditions, is identified. This is a relevant scientific and technical problem. The object of the study is the model of moving object detection from a mobile vision system.
Objective. The objective of this article is the analysis of the modern methods of moving object identification and the creation of
a new method. The method must allow observation from a mobile vision system and must be able to adapt to changing observation conditions.
Method. A method for identifying objects moving in space from a mobile vision system is proposed, which allows to
automatically detect moving objects, determine their three-dimensional coordinates with a given accuracy, and adapt to changing
observation conditions. This method is based on the developed mathematical model of stereoscopic determination of motion
parameters of objects in space, which allows us to increase the detection accuracy.
Results. The proposed method is implemented in software. An experiment confirming the adequacy of this mathematical model
was conducted. As the result of the experiment, data on the movement of the object and the mobile coordinate system were obtained.
Conclusions. The experiments have confirmed the performance of the proposed method and allow us to recommend it when
building mobile automatic tracking and identification systems for objects. The method allows automatic isolation of the moving
objects, determining their three-dimensional coordinates, and adapting to changing observation conditions. Prospects for further
research may be in the creation of hardware tools for the selection of moving objects, allowing to improve the accuracy of the

Author Biographies

A. G. Spevakov, Southwest State University, Kursk

PhD, Associate Professor of the Information Security Department

S. V. Spevakova, Southwest State University, Kursk

Post-graduate student of the Computer Science Department

I. S. Matiushin, Southwest State University, Kursk

Student of the Information Security Department


Spevakov A. G., Rubanov A. F., Zhukovskiy D. V. Realtime dynamic parameters, Information and

telecommunication technologies in intelligent systems: Second international conference, Barcelona, 10–12 May

, proceedings. Barcelona: International Academy of Informatization, 2004, pp. 177–180.

Verma R. A Review of object detection and tracking methods, International Journal of Advance Engineering and

Research Development, 2017, No. 4, pp. 569–578.

Titov D. V., Shirabakina T. A. Integrated optoelectronic devices for recognition of complex objects, Medical and

environmental information technologies, Kursk, 25–26 October 2014, proceedings. Kursk, Southwestern State

University, 2014, pp. 134–136.

Li J., Wan J. Robust object tracking based on sparse eigenbasis, IET Computer Vision, 2014, No. 8, pp. 601–610.

Yemelyanov S. G., Titov D. V. Embedded optical electronic image recognition devices in the multidimensional feature

space. Textbook. Kursk, South-West State University, 2013, 130 p.

Sharma K., Thakur N. A review and an approach for object detection in images, International Journal of Computational

Vision and Robotics, 2017, No. 7, pp. 196–197.

Wu J., Peng B., Huang Z. et al. Research on computer vision-based object detection and Classification, CCTA,

, No. 1, pp. 10–12.

Tiwari M., Singhai R. A video sequences, International Journal of Computational Intelligence Research, 2017,

No. 5, pp. 745–765.

Trufanov M. I., Boletsky E. B., Frolov M. M. et al. Vision system based on mobile transport robots, Information

technologies and mathematical modeling of systems:Odintsovo, 1–3 March 2017: proceedings. Odintsovo,

Federal State Budgetary Institution of Science “Center for Information Technologies in the Design of the Russian

Academy of Sciences”, 2017, pp. 164–168.

Polunin A. V., Trufanov M. I., Titov V. S. Binocular system of technical vision with a video sensor with variable

focal length for a mobile robot, News of the Volgograd State Technical University, 2014, No. 1, pp. 83–87.

Kumar P., Chakraborty R., Sarkar A. Robust object tracking under cluttered environment, International Journal of

Emergency Technology and Advanced Engineering, 2014, No. 1, pp. 18–19.

Boletsky E. B., Vakun V. V., Trufanov M. I. Binocular optoelectronic device with variable focal length, Bulletin of

Higher Educational Institutions. Instrument Engineering, 2015, No. 7, pp. 147–150.

Gridin V. N., Trufanov M. I., Pomelnikov A. V. et al. Optical-electronic device with variable focal length for

calculating the parameters of objects of a three-dimensional working scene (Varifocal binocular vision system for 3D

scene reconstruction), Information technologies in science, education and management: Moscow, 19–21 October 2016,

proceedings. Moscow, Limited Liability Company “Institute of New Information Technologies”, 2016, pp. 78–87.

Frolov M. M., Trufanov M.I. Structural and functional organization of a three-dimensional technical vision system

based on geographically distributed optical-electronic sensors, Modern problems of physical and mathematical

sciences, Oryol, 14–15 October 2017, proceedings. Oryol, Oryol State University, 2017, pp. 367–368.

Wenbo Y., Cao Z., Tan M. et al.] Multiple-object tracking in large-Scale scene, IEICE Transactions on Information and

Systems, 2016, No. 99, pp. 1903–1909.

Sakovich I. O., Belov Y. S. Overview of the main methods of contour analysis for the selection of the contours of

moving objects, Engineering Journal: Science and Innovations, 2014, № 12, pp. 35–38.

Bertinetto L., Valmadre J., Henriques J. et al. Fullyconvolutional Siamese networks for object tracking, IEEE,

, No. 9, pp. 85–89.

Shirabakina T. A., Spevakov A. G. Stereoscopic optoelectronic system for determining parameters of

dynamic objects in real time, Sensors and Systems, 2004, No. 1, pp. 65–67.

Cyganek B. Object detection and tracking, IEEE, 2013, No. 4, pp. 42–46.

Keuper M., Tang S., Andres B. et al. Motion segmentation and multiple object tracking by correlation co-clustering //

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, No. 1, pp. 1–10.

Spevakov A. G., Rubanov A. F. Stereoscopic opticalelectronic tracking system, News of Higher Educational

Institutions. Instrument making, 2005, No. 4, pp. 62–67.




How to Cite

Spevakov, A. G., Spevakova, S. V., & Matiushin, I. S. (2019). DETECTING OBJECTS MOVING IN SPACE FROM A MOBILE VISION SYSTEM. Radio Electronics, Computer Science, Control, (4), 103–110.



Neuroinformatics and intelligent systems