RESEARCH OF THE METHOD OF INCREASING THE OBJECT DETERMINATION ACCURACY ON THE LOW-RESOLUTION VIDEO STREAM
Main Article Content
Abstract
Study subject. The article proposes and investigates a method for increasing the accuracy of determination of the distance and the obstacle geometric parameters based on object contours determination using a computer vision system that uses low-resolution sensors. The goal is the effectiveness evaluation of the proposed method. Tasks: to conduct experimental researches of the quality indicators of the method of increasing the object contours determination accuracy; evaluate the effectiveness of this method. Used methods: statistical modeling, laboratory scale tests. The obtained results: the analysis of the proposed method efficiency was carried out and the influence of this method on the determination accuracy of the distance and object geometric parameters was evaluated. Conclusions: the considered method made it possible to achieve the increasing the determination accuracy of the distance and geometric object parameters by compensating for image blur using the Lucy-Richardson deconvolution algorithm. The obtained data showed a decrease in the maximum error in determining the distance from 8% to 4% and the error in the geometric object parameters from 7.7% to 5.8%. The implementation of this approach was carried out in the Python programming language.
Article Details
References
Dergachov, K., Krasnov, L., Cheliadin, O. and Plakhotnyi O. (2019), “Web-cameras stereo pairs color correction method and its practical implementation”, Advanced Information Systems, Vol. 3, No. 1, pp. 29–42, DOI: https://doi.org/10.20998/2522-9052.2019.1.06
Barsov, V., Kosterna, O. and Plakhotnyi O. (2020), “Research of the methods efficiency for determining the distance and geometric objects parameters of technical vision systems”, Advanced Information Systems, Vol. 4, No. 4, pp. 63-69, DOI: https://doi.org/10.20998/2522-9052.2020.4.09
Barsov, V. and Plakhotnyi O. (2018), “Determining the distance to the object and its geometric parameters for navigating the robot”, Control, navigation and communication systems, No. 4 (50), pp. 3–7, DOI: https://doi.org/10.26906/SUNZ.2018.4.003.
Bansal, Raghav, Raj, Gaurav and Choudhury, Tanupriya (2016), Blur image detection using Laplacian operator and Open-CV, IEEE Xplore, pp. 63-67, DOI: https://doi.org/10.1109/SYSMART.2016.7894491.
Panfilova, K. and Umnyashkin, S. (2016), “Linear blur compensation in digital images using Lucy-Richardson method”, IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW), pp. 302-304.
Herbert, Bay and Andreas, Ess, (2008), “TinneTuytelaars, Luc Van Gool “SURF: Speeded Up Robust Features”, Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346–359.
(2020), Deblurring Images Using the Lucy-Richardson Algorithm Homepage, available at: https://www.mathworks.com/help/images/deblurring-images-using-the-lucy-richardson-algorithm.html.
(2020), Camera Calibration in the program Camera Calibration Toolbox for Matlab, Homepage, available at: http://www.vision.caltech.edu/bouguetj/calib_doc/, last accessed 2020/04/05.
Joseph. Howse and Joe, Minichino (2015), Learning OpenCV 3 Computer Vision with Python, Second Edition, September 2015, Packt Publishing, ISBN: 9781785289774.
Saurabh, Kapur (2017), Computer Vision with Python 3, Packt Publishing, August, ISBN: 978-1-78829-976-3.
Prateek, Joshi (2015), OpenCV with Python By Example, Packt Publishing, September, ISBN: 978-1-78528-393-2.
(2020), Library for developing interfaces in Python, URL: https://doc.qt.io/qtforpython/
Lucy, L.B. (1974), “An iterative technique for the rectification of observed distributions”, The Astronomical journal, vol. 79, No. 6.
Richardson, W.H. (1972), “Bayesian-Based Iterative Method of Image Restoration”, Journal of the optical society of America, vol. 62, No. 6, pp. 55–59.