FOREGROUND DETECTION IN DYNAMIC SCENES OF INTELLIGENT TRANSPORT SYSTEMS

Authors

  • Volodymyr Kharchenko National Aviation University
  • Nataliia Kuzmenko National Aviation University
  • Alexander Kukush Taras Shevchenko National University of Kyiv

DOI:

https://doi.org/10.18372/2306-1472.80.14268

Keywords:

intelligent transport system, kernel density estimation, foreground, background, video stream

Abstract

The paper considers aspects of foreground detection in dynamic scenes of intelligent transport system based on computer vision and artificial intelligence. Traditional and recent background modeling models have been considered. Nonparametric approach for background subtraction based on the kernel model was used as the most appropriate in dynamic environment. The value of the estimated kernel density function for each pixel of original image was compared with threshold value, estimated by Otsu’s method. The proposed kernel density estimation method was verified on video-stream containing moving objects and indicated good performance for Unmanned Aerial Vehicles application.

Author Biographies

Volodymyr Kharchenko, National Aviation University

Doctor of Engineering. Professor. Vice-Rector on Scientific Work of the National Aviation University, Kyiv, Ukraine. Editor-in-Chief of the scientific journal Proceedings of the National Aviation University. Winner of the State Prize of Ukraine in Science and Technology, Honorable Worker of Science and Technology of Ukraine. Education: Kyiv Institute of Civil Aviation Engineers, Kyiv, Ukraine. Research area: management of complex socio-technical systems, air navigation systems and automatic decision-making systems aimed at avoidance conflict situations, space information technology design, air navigation services in Ukraine provided by СNS/АТМ systems.

Nataliia Kuzmenko, National Aviation University

Candidate of Engineering. Junior researcher. National Aviation University. Education: National Aviation University, Kyiv, Ukraine (2013). Research area: navigation and control of dynamic systems, artificial intelligence.

Alexander Kukush, Taras Shevchenko National University of Kyiv

Doctor of Physical and Mathematical Sciences. Professor. Faculty of Mechanics and Mathematics, Taras Shevchenko National University of Kyiv. Education: Taras Shevchenko Kyiv State University, Kyiv, Ukraine (1979). Research area: navigation and control of dynamical systems, mathematical and applied statistics, financial and actuarial mathematics.

References

Balaž, Zdenko, et al. "Intelligent Transport Systems (ITS) for sustainable mobility." (2014), UNECE, Geneva, 123 P.

Loce, R. P., Bala, R., Trivedi, M., & Wiley, J. (Eds.). (2017). Computer Vision and Imaging in Intelligent Transportation Systems. Wiley, 404 P. https://doi.org/10.1002/9781118971666

Sładkowski, Aleksander, and Wiesław Pamuła, eds. Intelligent transportation systems-problems and perspectives. Vol. 303. Springer International Publishing, 2016, 303 P. https://doi.org/10.1007/978-3-319-19150-8

Intelligent Transport Systems: when technology improves the quality of mobility. Available at: https://erticonetwork.com/intelligent-transport-systems-when-technology-improves-the-quality-of-mobility-2/.

V.P. Kharchenko, A.G. Kukush, N.S. Kuzmenko, and I.V. Ostroumov, "Probability density estimation for object recognition in Unmanned Aerial Vehicle application," in Actual problems of unmanned aerial vehicles development: APUAVD-2017 5th International Conference of IEEE, pp. 233-236, October 2017. https://doi.org/10.1109/APUAVD.2017.8308818

M. Piccardi.Background subtraction techniques: a review. IEEE International Conference on Systems, Man and Cybernetics. Vol4., 2004, pp. 3099-3104.

V. Kharchenko, A. Kukush, N. Kuzmenko, and I. Ostroumov, Probabilistic approach to object detection and recognition for videostream processing, Proceedings of the National Aviation University. Vol 71, No 2 (2017), NAU, 2017, p. 8-14. https://doi.org/10.18372/2306-1472.71.11741

T. Bouwmans. Traditional and recent approaches in background modeling for foreground detection: An overview.Computer Science Review 11, 2014, pp. 1-66. https://doi.org/10.1016/j.cosrev.2014.04.001

A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. In European conference on computer vision, 2000, pp. 751-767 https://doi.org/10.1007/3-540-45053-X_48

A.Pérez, P.Larrañaga, and I. Inza. Bayesian classifiers based on kernel density estimation: Flexible classifiers. International Journal of Approximate Reasoning, 50(2), 2009, pp. 341-362. https://doi.org/10.1016/j.ijar.2008.08.008

M. Narayana, A. Hanson, and E. Learned-Miller. Background subtraction separating the modeling and the inference. Machine Vision and Applications, 2014, pp. 1163-1174. https://doi.org/10.1007/s00138-013-0569-y

A. Tavakkoli, M. Nicolescu, G. Bebis, and M. Nicolescu. Non-parametric statistical background modeling for efficient foreground region detection. Machine Vision and Applications, Vol. 20(6), 2009, pp. 395-409. https://doi.org/10.1007/s00138-008-0134-2

B. Zhong, S. Liu, and H. Yao. Local spatial co-occurrence for background subtraction via adaptive binned kernel estimation. Asian Conference on Computer Vision, ACCV 2009, 2009, pp.152-161. https://doi.org/10.1007/978-3-642-12297-2_15

Y. Chen. A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology, 1(1), 2017, pp.161-187. https://doi.org/10.1080/24709360.2017.1396742

Khushbu and I. Vats, "Otsu Image Segmentation Algorithm: A Review," International Journal of Innovative Research in Computer and Communication Engineering, vol. 5(6), pp. 11945- 11948, June 2017.

N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE transactions on systems, man, and cybernetics, vol. SMC-9, no. 1, pp. 62-66, January 1979. https://doi.org/10.1109/TSMC.1979.4310076

Downloads

Published

11-12-2019

How to Cite

Kharchenko, V., Kuzmenko, N., & Kukush, A. (2019). FOREGROUND DETECTION IN DYNAMIC SCENES OF INTELLIGENT TRANSPORT SYSTEMS. Proceedings of National Aviation University, 80(3), 14–20. https://doi.org/10.18372/2306-1472.80.14268

Issue

Vol. 80 No. 3 (2019)

Section

AEROSPACE SYSTEMS FOR MONITORING AND CONTROL

License