OBJECT DETECTION PERFORMANCE INDICATOR IN VIDEO SUVEILLANCE SYSTEMS

Authors

  • I. S. Katerynchuk Bohdan Khmelnytskyi National Academy of the State Border Guard Service of Ukraine, Khmelnytskyi, Ukraine, Ukraine
  • A. O. Babaryka Bohdan Khmelnytskyi National Academy of the State Border Guard Service of Ukraine, Khmelnytskyi, Ukraine, Ukraine
  • R. P. Khoptinskiy National Academy of the State Border Service of Ukraine named after Bohdan Khmelnytskyi, Khmelnytskyi, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2023-2-8

Keywords:

probability, detection, human operator, criterion, efficiency, indicator, task, performance, calculations, mathematical apparatus

Abstract

Context. The probability of detecting the object by the operator of the video surveillance system depends on a number of parameters (geometric dimensions of the object of observation, distance to the object of observation, parameters of the video surveillance camera, monitor parameters, etc.).

Objective. The purpose of the article is to develop an indicator of the effectiveness of detecting dynamic objects when evaluating the functioning of video surveillance systems.

Method. An indicator of the effectiveness of object detection when evaluating the functioning of video surveillance systems is proposed. The proposed indicator is expressed in the probability of detection of the object of interest by the i-th operator thanks to the person’s own visual apparatus or with the help of a software algorithm. This indicator differs from the existing ones by taking into account the parameters of the optical system, the parameters of the information display device (monitor), the number of video surveillance cameras, etc. The developed indicator makes it possible to estimate the probability of detection of an object by a video surveillance system operator thanks to a person's own visual apparatus or with the help of a software algorithm, depending on the distance to such an object.

Results. According to the results of experimental calculations, it has been proven that the effectiveness of the use of video surveillance systems with the use of video analytics functions (using the example of the dynamic object detection algorithm).

Conclusions. The conducted experimental calculations confirmed the efficiency of the proposed mathematical apparatus and allow us to recommend it for use in practice when solving problems of evaluating the effectiveness of the functioning of video surveillance systems.

Author Biographies

I. S. Katerynchuk, Bohdan Khmelnytskyi National Academy of the State Border Guard Service of Ukraine, Khmelnytskyi, Ukraine

Dr. Sc., Professor, Professor of the Department of Telecommunication and Information Systems

A. O. Babaryka, Bohdan Khmelnytskyi National Academy of the State Border Guard Service of Ukraine, Khmelnytskyi, Ukraine

PhD, Associate Professor of the Department of Telecommunication and Information Systems

R. P. Khoptinskiy, National Academy of the State Border Service of Ukraine named after Bohdan Khmelnytskyi, Khmelnytskyi, Ukraine

PhD, Associate Professor of the Department of Telecommunications and Information Systems

References

Pikaar R., Dick L. Human factors guidelines for CCTV control center design introduction to a symposium, 11th International Symposium on Human Factors in Organisational Design and Management, 2014, pp. 135– 140. DOI: 10.4122/DTU:2131.

Johnson J. Analysis of image forming systems, Proceedings of the Image Intensifier Symposium. USA, Virginia, 1958, pp. 249–273.

Vollmerhausen R. H., Jacobs E. The targeting task performance (TTP) metric. A new model for predicting target acquisition performance. Technical report. AMSELNV-TR-230, 2004, 125 p.

Bareła J. Kastek M., Firmanty K., Trzaskawka P., Dulski R., Kucharz J. Determination of range parameters of observation devices, Electro-Optical and Infrared Systems: Technology and Applications, IX, 85411D, 2012. DOI: 10.1117/12.974487.

IEC 62676-4:2014. Video surveillance systems for use in security applications. Part 4, Application guidelines, 2014, 143 p.

Logan Des Autels G. A modern review of the johnson image resolution criterion, Optik, 2022, Vol. 249. DOI: 10.1016/j.ijleo.2021.168246.

Hollands J. G., Terhaar P., Pavlovic N. J. Effects of Resolution, Range, and Image Contrast on Target Acquisition Performance, Human Factors, 2018, Vol. 60(3). pp. 363–383. DOI: 10.1177/0018720818760331.

Waldman G., Wootton J. Electro-optical Systems modeling, 1992, 256 p.

Gerald C. Electro-Optical Imaging System Performance, fifth edition, JCD, Publishing and SPIE Press, 2008, 538 p.

Packard C. D., Curran A. R., Saur N. E., Rynes P. L. Simulation-based sensor modeling and at-range target detection characterization with MuSES, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing, 2015, Vol. 9452, P. 143–157. DOI : 10.1117/12.2177310.

Xiaoqian Z., Hanshan L. Research on target capture probability calculation model of composite photoelectric detection imaging sensor system, Optik, 2018, Vol. 166, pp. 161–168. DOI : 10.1016/j.ijleo.2018.04.037.

Younis O., Al-Nuaimy W., Alomari M., Rowe F. A hazard detection and tracking system for people with peripheral vision loss using smart glasses and augmented reality, International Journal of Advanced Computer Science and Applications, 2019, Vol. 10, No. 2, pp. 1–9. DOI : 10.14569/IJACSA.2019.0100201.

Babaryka A. et al. Research of the efficiency dynamic objects detecting on the video sequence from video surveillance cameras, Paradigm of Knowledge, 2020, Vol. 6 (44), pp. 35–44. DOI : 10.26886/2520-7474.6(44)2020.3.

Barnich O., Droogenbroeck M. Van ViBe: A universal background subtraction algorithm for video sequences, IEEE Transactions on Image Processing : proceedings, 2011, Vol. 20, No. 6, pp. 1709–1724. DOI : 10.1109/TIP.2010.2101613.

Chevalier P. On the specification of the DRI requirements for a standard NATO target, 2015, 14 p. DOI : 10.13140/RG.2.1.4833.9604.

Triantaphillidou S. et al. Contrast sensitivity in images of natural scenes, Signal Processing : Image Communication, Vol. 75, 2019, pp. 64–75. DOI : 10.1016/j.image.2019.03.002.

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Published

2023-06-30

How to Cite

Katerynchuk, I. S., Babaryka, A. O., & Khoptinskiy, R. P. (2023). OBJECT DETECTION PERFORMANCE INDICATOR IN VIDEO SUVEILLANCE SYSTEMS . Radio Electronics, Computer Science, Control, (2), 72. https://doi.org/10.15588/1607-3274-2023-2-8

Issue

No. 2 (2023): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

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Copyright (c) 2023 І. С. Катеринчук, А. О. Бабарика, Р. П. Хоптинський

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