Effect of morphological filters on the moving object detection process
DOI:
https://doi.org/10.61799/2216-0388.676Keywords:
back ground subtraction, morphological filters, objectdetection, image processingAbstract
In the processes of background subtraction applied to the detection of moving objects, one of the most relevant stages is the filtering by morphology, where the image is simplified and most of the shape characteristics of the objects are preserved. Therefore, a comparison is made between the operations of dilation, erosion, opening, closing and gradient in video images with static background, where people circulate in uncontrolled environments, to determine their behavior in the detection and counting of people. Image processing is performed in Python language and the specialized computer vision package OpenCV is used. In addition, a graphical user interface was developed using Tkinter to enter the values of the size and shape of the structural element for processing. When applying the morphological filtering by dilatation, a success in the detections of 82.28 %, with erosion the accuracy was 81.86 %, while, with the opening, closing and gradient operations the accuracy was 83.69 %, 93.07 % and 87.69 % respectively.
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A. Mehdizadeh, M.M. Disfani, R. Evans, A. Arulrajah, and D.E.L. Ong, “Application of image processing in internal erosion investigation,” ICSMGE 2017 - 19th Int. Conf. Soil Mech. Geotech. Eng., vol. 2017-Septe, no. September, pp. 2925–2928, 2017
C. Shan, B. Huang, and M. Li, “Binary Morphological Filtering of Dominant Scattering Area Residues for SAR Target Recognition,” Comput. Intell. Neurosci., vol. 2018, 2018, doi: 10.1155/2018/9680465
X. Wang, Q. Zhao, and J. Tan, “Improved Morphological Band-Pass Filtering Algorithm and Its Application in Circle Detection,” Math. Probl. Eng., vol. 2018, 2018, doi: 10.1155/2018/3765164
J.A.M. Saif, M.H. Hammad, and I.A.A. Alqubati, “Gradient Based Image Edge Detection,” Int. J. Eng. Technol., vol. 8, no. 3, pp. 153–156, 2016, doi: 10.7763/ijet.2016.v6.876
L. Dang, G. Tewolde, X. Zhang, and J. Kwon, “Reduced resolution lane detection algorithm,” 2017 IEEE AFRICON Sci. Technol. Innov. Africa, AFRICON 2017, pp. 1459–1464, 2017, doi: 10.1109/AFRCON.2017.8095697
L. Neumann and A. Vedaldi, “Tiny People Pose,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11363 LNCS, pp. 558–574, 2019, doi: 10.1007/978-3-030-20893-6_35
J.D. Arias Hernández, A.F. Jiménez López, and H.O. Porras Castro, “Desarrollo de aplicaciones en python para el aprendizaje de física computacional,” Ing. Investig. y Desarro., vol. 16, no. 1, p. 72, 2016, doi: 10.19053/1900771x.5122
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2021-01-01
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Castro-Casadiego, S. A., Sánchez-Mojica, K. Y., Puerto-López, K. C., Niño-Rondón, C. V., Medina-Delgado, B., & Guevara-Ibarra, D. (2021). Effect of morphological filters on the moving object detection process. Mundo FESC Journal, 11(21), 87–95. https://doi.org/10.61799/2216-0388.676
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