D.A. Loktev – Ph.D. (Eng.), Associate Professor, 

Department “Information Systems and Telecommunications”, Bauman Moscow State Technical University

E-mail: loktevdan@bmstu.ru

V.A. Kochnev − Ph.D. (Eng.), 

Department “Transport construction”, Russian University of Transport (Moscow)

E-mail: nttmag@mail.ru

A.A. Loktev − Ph.D. (Eng.), Professor, Head of the Department “Transport construction”, 

Russian University of Transport (Moscow)

E-mail: aaloktev@yandex.ru

This research is devoted to the development of a model of the object image blurring function on an image that represents primary information for processing in an automated monitoring and control system in order to detect, recognize and detect parameters of moving and stationary objects. The presented model is based on two different aspects that represent the nature of the appearance and influence of the indistinctness of their contours on the displayed images. It is taken into account that blurring can both limit the capabilities of monitoring and control systems, causing various errors when detecting and recognizing an object, and provide the ability to determine parameters of its state and behavior. The proposed mathematical model of blurring takes into account seven main factors: the parameters of the environment from the monitoring system to the object under study; the dependence of blurring on color; features of movement of elements of the detection system; the state of the background; settings of detection tools; primary processing of the object image; the state and behavior of the object under study. The blur function allows you to determine the minimum distance between detected objects, the maximum speed of movement of objects for their recognition, and the parameters of photo and video detectors necessary for working in an automated system for remote monitoring and control in real conditions, taking into account external factors and the nature of the behavior of the diagnostic object. The proposed model of image blurring will allow us to develop mathematical and algorithmic software that allows us to increase the probability of detecting, recognizing and detecting parameters of control objects in remote monitoring systems.

1. Gonsales R., Vuds R. Cifrovaja obrabotka izobrazhenij. M.: Tehnosfera. 2012. 1104 s. (in Russian).
2. Bojko I.A., Gur'janov R.A. Raspoznavanie ob’ektov na osnove videosignala, poluchennogo s kamery, ustanovlennoj na podvizhnoj platforme. Molodoj uchenyj. 2013. № 6. S. 34−36 (in Russian).
3. Tan X., Triggs B. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. IEEE Transactions on image processing. 2010. V. 19. № 6. P. 1635−1650.
4. Sun Z., Bebis G., and Miller R. On-road vehicle detection using optical sensors: A review. Proceeding of the IEEE International Conference on Intelligent Transportation Systems, 2004. V. 6. P. 125−137.
5. Wiedemann M., Sauer M., Driewer F., Schilling K. Analysis and characterization of the PMD camera for application in mobile robotics. Proceedings of the 17th World Congress the International Federation of Automatic Control. Seoul, Korea. July 6-11, 2008. P. 46−51.
6. Favorskaja M.N., Shilov A.S. Algoritmy realizacii ocenki dvizhenija v sistemah videonabljudenija. Sistemy upravlenija i informacionnye tehnologii. M.-Voronezh: IPU RAN, VGTU. 2008. № 3.3(33). S. 408–412 (in Russian).
7. Robertson G., Mackinlay J., Card S. Information visualization using 3D interactive animation. Communications of the ACM. 1993. V. 36. № 4. P. 57−71.
8. Lelegard L., Vallet B., Bredif M. Multiscale Haar transform for blur estimation from a set of images. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science. Munich, Germany. October 5−7, 2011. P. 65−70.
9. Lindner M., Kolb A. Calibration of the intensity-related distance error of the Pmd Tof-camera. In Proceedings of Spie, Intelligent Robots and Computer Vision. Boston, MA, USA. 2007. V. 6764. P. 56−64.
10. Kol'cov P.P. Ocenka razmytija izobrazhenija. Komp'juternaja optika. 2011. T. 35. № 1. S. 95−102 (in Russian).
11. Braslavskaja O.B., Gendrina I.Ju., Kvach A.S. Sravnenie dvuh metodov rascheta funkcii razmytija tochki i opticheskoj peredatochnoj funkcii. Izvestija VUZov. Ser. Fizika. 2013. T. 56. № 9−2. S. 215−216 (in Russian).
12. Grigor'ev A.V., Trusov V.A., Bannov V.Ja., Andreev P.G., Tan'kov G.V. Modelirovanie sleda razmytija izobrazhenija krugloj metki pri ee komplanarnom i ortogonal'nom vibroperemeshhenijah. Sb. trudov Mezhdunar. simpoziuma «Nadezhnost' i kachestvo». 2015. T. 1.  S. 107−109 (in Russian).
13. Loktev A.A., Loktev D.A. Metod opredelenija rasstojanija do ob’ekta putem analiza razmytija ego izobrazhenija. Vestnik MGSU. 2015. № 6. S. 140−151 (in Russian).
14. Loktev A.A., Alfimcev A.N., Loktev D.A. Algoritm razmeshhenija videokamer i ego programmnaja realizacija. Vestnik MGSU. 2012. № 5. S. 167−175 (in Russian).
15. Shlej M.D., Rogov A.A., Borisov A.Ju. Metody i algoritmy raspoznavanija ob’ektov sel'skih poselenij na cifrovoj karte. Matematicheskie metody raspoznavanija obrazov. 2011. T. 15. № 1. S. 571−574 (in Russian).
16. Loktev A.A., Sycheva A.V., Zapol'nova E.V., Sychev V.P., Dmitriev V.G. Issledovanie osobennostej dinamicheskoj reakcii verhnego stroenija zheleznodorozhnogo puti ot podvizhnogo sostava na osnove modeli transversal'no-izotropnoj plastiny na deformiruemom osnovanii. Problemy mashinostroenija i avtomatizacii. 2018. № 2. S. 55−65 (in Russian).
17. Vytovtov A.V., Shumilin V.V., Kalach A.V. Raspoznavanie oblasti gorenija na cherno-belom staticheskom izobrazhenii, poluchennom s borta bespilotnogo vozdushnogo sudna. Tehnosfernaja bezopasnost'. 2018. № 2 (19). S. 12−24 (in Russian).
18. Loktev D.A., Loktev A.A. Determination of object location by analyzing the image blur. Contemporary Engineering Sciences. 2015.  Т. 8. № 9. С. 467−475 (in Russian).
19. Loktev D.A., Loktev A.A. Development of a user interface for an integrated system of video monitoring based on ontologies. Contemporary Engineering Sciences. 2015. V. 8. № 20. P. 789−797.
20. Loktev A.A., Sychev V.P., Loktev D.A. K zadache proektirovanija modulja vizual'nogo raspoznavanija jelementov verhnego stroenija puti na vysokoskorostnyh magistraljah. Transport Rossijskoj Federacii. 2017. № 1 (68). S. 22−26 (in Russian).
21. Loktev D.A., Alfimcev A.N. Izmerenie rasstojanija do dvizhushhegosja ob’ekta s pomoshh'ju kompleksnoj sistemy videomonitoringa. Inzhenernyj zhurnal: nauka i innovacii. 2013. № 11 (23). S. 4 (in Russian).
22. Loktev D.A., Bykov Ju.A., Kovalenko N.I. Ispol'zovanie metoda analiza razmytija izobrazhenija dlja opredelenija vneshnih defektov zheleznodorozhnogo puti. Nauka i tehnika transporta. 2016. № 1. S. 69−75 (in Russian).
23. Loktev D.A. Opredelenie parametrov ob#ekta po serii ego izobrazhenij v kompleksnoj sisteme monitoring. Put' i putevoe hozjajstvo. 2015. № 2. S. 31−33 (in Russian).
24. Loktev A.A., Loktev D.A. Vyjavlenie i detektirovanie vneshnih defektov verhnego stroenija puti agregirovannym metodom na osnove stereozrenija i analiza razmytija obraza. Vnedrenie sovremennyh konstrukcij i peredovyh tehnologij v putevoe hozjajstvo. 2017. T. 11. № 11(11). S. 96−100 (in Russian).
25. Loktev D.A., Loktev A.A. Diagnostics of external defects of railway infrastructure by analysis of its images. Proceedings − 2018 Global Smart Industry Conference «GloSIC-2018». 2018. С. 8570083 (in Russian).
26. Loktev A.A., Loktev D.A. Ocenka izmerenij rasstojanija do ob’ekta pri issledovanii ego graficheskogo obraza. Vestnik MGSU. 2015.  № 10. S. 54−65 (in Russian).
27. Loktev A.A., Loktev D.A. Sostavlenie raschetnoj modeli rekonstruiruemyh transportnyh ob’ektov istoriko-arhitekturnogo nasledija. Nauka i tehnika transporta. 2017. № 4. S. 71−77 (in Russian).