Communications - Scientific Letters of the University of Zilina 2019, 21(3):28-34 | DOI: 10.26552/com.C.2019.3.28-34

Determination of the Dynamic Vehicle Model Parameters by Means of Computer Vision

Daniil A. Loktev1, Alexey A. Loktev2, Alexandra V. Salnikova3, Anna A. Shaforostova4
1 Department of Information Systems and Telecommunications, Bauman Moscow State Technical University, Moscow, Russia
2 Department of Transport Construction, Russian University of Transport (MIIT), Moscow, Russia
3 International Laboratory of Statistics of Stochastic Processes and Quantitative Finance of Tomsk State University, Russia and Department of Management Systems and Information Technologies in Building, Voronezh State Technical University, Russia
4 Department of Radio Engineering, Voronezh State Technical University, Russia

This study is devoted to determining the geometric, kinematic and dynamic characteristics of a vehicle. To this purpose, it is proposed to use a complex approach applying the models of deformable body mechanics for describing the oscillatory movements of a vehicle and the computer vision algorithms for processing a series of object images to determine the state parameters of a vehicle on the road. The model of the vehicle vertical oscillations is produced by means of the viscoelastic elements and the dry friction element that fully enough represent the behavior of the sprung masses. The introduced algorithms and models can be used as a part of a complex system for monitoring and controlling the road traffic. In addition, they can determine both the speed of the car and its dynamic parameters and the driving behavior of the individual drivers.

Keywords: vehicle; mode of vertical oscillations; Kelvin-Voight and Maxwell viscoelastic elements; image processing; feature point method; Haar primitives; errors of the first and second kind; car motion parameters

Received: February 18, 2019; Accepted: March 13, 2019; Published: August 15, 2019  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Loktev, D.A., Loktev, A.A., Salnikova, A.V., & Shaforostova, A.A. (2019). Determination of the Dynamic Vehicle Model Parameters by Means of Computer Vision. Communications - Scientific Letters of the University of Zilina21(3), 28-34. doi: 10.26552/com.C.2019.3.28-34
Download citation

References

  1. AZIMOV, S. Z., LEBEDEV, O. V., SHERMUKHAMEDOV, A. A. Optimization of calculation of the process of braking of wheel machines (in Russian). 8th Russian Congress of Theoretical and Applied Mechanics : proceedings. 2001, p. 211.
  2. SOUKUP, J., SKOCILAS, J., SKOCILASOVA, B. Vertical vibration of the vehicle model with higher degree of freedom. Procedia Engineering [online]. 2014, 96, p. 435-443. ISSN 1877-7058. Available from: https://doi.org/10.1016/j.proeng.2014.12.113 Go to original source...
  3. DEVYATERIKOV, E. A., MIKHAILOV, B. B. Vision system for measuring the path of a mobile robot (in Russian). Mechanics, Control and Informatics. 2012, 2(8), p. 219-224. ISSN 2075-6836.
  4. ZAMOTAYLOV, O. V. Problems of image recognition of the device of subsurface radar on the basis of mobile road laboratory (in Russian). T-Comm-Telecommunications and Transport. 2010, 6, p. 38-42. ISSN 2072-8735, eISSN 2072-8743.
  5. LOKTEV D. A. Determination of object parameters by a series of its images in the integrated monitoring system (in Russian). Path and Track Facilities. 2015, 2, p. 31-33. ISSN 0131-5765
  6. SUN, Z., BEBIS, G., MILLER, R. On-road vehicle detection using optical sensors: A review. 7th International IEEE Conference on Intelligent Transportation Systems : proceedings [online]. 2004. ISBN 0-7803-8500-4. Available from: https://doi.org/10.1109/ITSC.2004.1398966 Go to original source...
  7. BEDER, C., BARTCZAK, B., KOCH, R. A comparison of PMD-cameras and stereo-vision for the task of surface reconstruction using patchlets. 2007 IEEE Conference on Computer Vision and Pattern Recognition : proceedings [online]. Vol. 4. IEEE Service Center : Piscataway, NJ, 2007. ISBN 1-4244-1179-3, eISBN 1-4244-1180-7, p. 17-22. Available from: https://doi.org/10.1109/CVPR.2007.383348 Go to original source...
  8. WIEDEMANN, M., SAUER, M., DRIEWER, F., SCHILLING, K. Analysis and characterization of the PMD camera for application in mobile robotics. 17th World Congress - International Federation of Automatic Control : proceedings [online]. 2008. ISBN 978-1-1234-7890-2. Available from: https://doi.org/10.3182/20080706-5-KR-1001.3304
  9. LITOMISKY, K. Consumer RGB-D cameras and their applications [online]. Ph.D. Thesis. University of California: Riverside, 2012. Available from: http://alumni.cs.ucr.edu/~klitomis/files/RGBD-intro.pdf
  10. HAHNE, U. Real-time depth imaging [online]. Ph.D. Thesis. Technische Universitat Berlin: Berlin, 2012. Available from: https://d-nb.info/1023762218/34
  11. GIL, P., POMARES, J., TORRES, F. Analysis and adaptation of integration time in PMD camera for visual serving. 20th International Conference on Pattern Recognition : proceedings [online]. 2010. ISSN 1051-4651. Available from: https://doi.org/10.1109/ICPR.2010.85 Go to original source...
  12. SINGH S., WEST J. Cyclone: A laser scanner for mobile robot navigation. Technical Report CMU-RI-TR-91-18. The Robotics Institute, Carnegie Mellon University: Pittsburgh, 1991.
  13. DANKO, M., TARABA, M., ADAMEC, J., DRGONA, P. Visualization of Scoda instrument cluster. Communications - Scientific Letters of the University of Zilina [online]. 2018, 20(1), p. 27-31. ISSN 1335-4205, eISSN 2585-7878. Available from: http://komunikacie.uniza.sk/index.php/communications/article/view/39 Go to original source...
  14. VELAS, A., LOVECEK, T., VALOUCH, J., DWORZECKI, J., VNENCAKOVA, E. Testing radio signal range of selected components. Communications - Scientific Letters of the University of Zilina [online]. 2018, 20(2), p. 68-77. ISSN 1335-4205, eISSN 2585-7878. Available from: http://komunikacie.uniza.sk/index.php/communications/article/view/90 Go to original source...
  15. BOROS, M., SISER, A., KEKOVIC, Z., MAZAL, J. Mechanical characteristics of cylinder pin tumbler locks as they relate to resistance testing. Communications - Scientific Letters of the University of Zilina [online]. 2018, 20(2), p. 96-101. ISSN 1335-4205, eISSN 2585-7878. Available from: http://komunikacie.uniza.sk/index.php/communications/article/view/94 Go to original source...
  16. BENDER, C., DENKER, K., FRIEDRICH, M., HIRT, K., UMLAUF, G. A hand-held laser scanner based on multi-camera stereo-matching. IRTG 1131 - Visualization of Large and Unstructured Data Sets Workshop 2011 : proceedings [online]. 2011. Available from: https://doi.org/10.4230/OASIcs.VLUDS.2011.123
  17. DESCHENES, F., ZIOU, D. Depth from defocus estimation in spatial domain. Computer Vision and Image Understanding [online]. 2001, 81(2), p. 143-165. ISSN 1077-3142. Available from: https://doi.org/10.1006/cviu.2000.0899 Go to original source...
  18. RAJABZADEH, T., VAHEDIAN, A., POURREZA, H. R. Static object depth estimation using defocus blur levels features. 6th International Conference on Wireless Communications Networking and Mobile Computing : proceedings. 2010. ISBN 978-1-4244-3709-2. Go to original source...
  19. BORACHI, G., CAGLIOTI, V. Motion blur estimation at corners. 3rd International Conference on Computer Vision Theory and Applications : proceedings. 2008. ISBN 978-989-8111-21-0.
  20. ZHUO, S., SIM, T. Defocus map estimation from a single image. Pattern Recognition [online]. 2011, 44(9), p. 1852-1858. ISSN 0031-3203. Available from: https://doi.org/10.1016/j.patcog.2011.03.009 Go to original source...
  21. ELDER, J. H., ZUCKER, S. W. Local scale control for edge detection and blur estimation. IEEE Transaction on Pattern Analysis and Machine Intelligence [online]. 1998, 20(7), p. 120-127. ISSN 0162-8828, eISSN 1939-3539. Available from: https://doi.org/10.1109/34.689301 Go to original source...
  22. LOSCH, S. Depth from blur combining image deblurring and depth estimation. Bachelor's Thesis. Saarland University: Homburg, 2009.
  23. LIN, H.-Y., CHANG, C.-H. Depth from motion and defocus blur. Optical Engineering [online]. 2006, 45(12), p. 127201-1-127201-12. ISSN 0091-3286, eISSN 1560-2303. Available from: https://doi.org/10.1117/1.2403851 Go to original source...
  24. ALEXIEV K., NIKOLOVA I., ZAPRYANOV G. 3D scenes recovery through an active camera based on blur assessment of the resulting image. Information Technologies and Control. 2008, 3-4, p. 10-20. ISSN 1392-124X, eISSN 2335-884X.
  25. LOKTEV, D. A., ALFIMTSEV, A. N., LOKTEV, A. A. Modeling of an integrated system of video monitoring inside the building. Part 2. The Algorithm of Recognition of Objects (in Russian). Vestnik MGSU. 2012, 5, p. 124-131. ISSN 1997-0935, eISSN 2304-6600.
  26. LUCAS, B. D., KANADE, T. An iterative image registration technique with an application to stereo vision. 7th International Joint Conference on Artificial Intelligence : proceedings. 1981.
  27. BOUGUET J.-Y. Pyramidal implementation of the lucas-kanade feature tracker. Intel Corporation Microprocessor Research Labs: Santa Clara, CA, USA, 2000.
  28. HARRIS, C., STEPHENS, M. A combined corner and edge detector. 4th Alvey Vision Conference : proceedings [online]. 1988. Available from: https://doi.org/10.5244/C.2.23 Go to original source...
  29. LOKTEV, A. A., SYCHEV, V. P., LOKTEV, D. A., DMITRIEV, V. G. Automated system for identifying defects in wheels of the rolling stock on the basis of assessment of shock nonaxisymmetric impact of wheels on rail when modeling the track structure with orthotropic plate (in Russian). Engineering and Automation Problems. 2017, 4, p. 59-70. ISSN 0234-6206.
  30. VIOLA P., JONES M. J. Robust real-time object detection. Second Workshop on Statistical and Computational Theories of Vision: procedings. 2001.
  31. CHERNOYAROV, O. V., VACULIK, M., SHIRIKYAN, A., SALNIKOVA, A. V. Statistical analysis of fast fluctuating random signals with arbitrary-function envelope and unknown parameters. Communications - Scientific Letters of the University of Zilina [online]. 2015, 17(1A), p. 35-43. ISSN 1335-4205, eISSN 2585-7878. Available from: http://komunikacie.uniza.sk/index.php/communications/article/view/410 Go to original source...
  32. LELEGARD, L., VALLET, B., BREDIF, M. Multiscale haar transform for blur estimation from a set of images. ISPRS Conference PIA 2011. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science : proceedings [online]. 2011. Available from: https://doi.org/10.5194/isprsarchives-XXXVIII-3-W22-65-2011 Go to original source...
  33. LOKTEV, D. A., LOKTEV A. A. Determination of object location by analyzing the image blur. Contemporary Engineering Sciences [online]. 2015, 8(9), p. 467-475. ISSN 1313-6569, eISSN 1314-7641. Available from: http://dx.doi.org/10.12988/ces.2015.52198 Go to original source...

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.