FOREGROUND DETECTION IN DYNAMIC SCENES OF INTELLIGENT TRANSPORT SYSTEMS
Keywords:intelligent transport system, kernel density estimation, foreground, background, video stream
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.
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