An Application of Rule-Based Classification with Fuzzy Logic to Image Subtraction

An Application of Rule-Based Classification with Fuzzy Logic to Image Subtraction

Автор: Marlon D. Hernandez

Журнал: International Journal of Engineering and Manufacturing @ijem

Статья в выпуске: 2 vol.13, 2023 года.

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Surveillance camera is used as a new technology for security. In this research, the combination of OpenCV with image processing will be discussed. Saving the space in the hard drive by recording only video when here would be an image formed in the subtraction of the original image to the next image captured. With the use of Image Processing and Fuzzy logic, the research was enhanced by eliminating the recording of same image captured. After analyzing the background images, it can now determine when to start recording the video or when to stop recording a video by subtracting the images in the backdrop image and comparing the image if there was an object in motion using template matching. With the application of the project, memory storage saved up to forty-six percentage points.

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Image Processing, Surveillance Camera, OpenCV, Fuzzy Logic, Rule-Based

Короткий адрес: https://sciup.org/15018689

IDR: 15018689   |   DOI: 10.5815/ijem.2023.02.03

Список литературы An Application of Rule-Based Classification with Fuzzy Logic to Image Subtraction

  • G. Constantino, “DEPED COMPUTERIZATION PROGRAM,” 2018. https://www.deped.gov.ph/2018/04/06/deped-computerization-program/
  • Y. Bai et al., “Impact of online computer assisted learning on education: Experimental evidence from economically vulnerable areas of China,” Econ. Educ. Rev., vol. 94, p. 102385, Jun. 2023, doi: 10.1016/j.econedurev.2023.102385.
  • S. J. Lee, G. M. Francom, and J. Nuatomue, “Computer science education and K-12 students’ computational thinking: A systematic review,” Int. J. Educ. Res., vol. 114, p. 102008, 2022, doi: 10.1016/j.ijer.2022.102008.
  • M. T. Montemayor, “Return stolen computers: DepEd,” 2018. https://www.pna.gov.ph/articles/1048448
  • Department of education, “DepEd, PNP foil illegal online sale of DepEd-issued laptop; warns of legal action vs theft of gov’t property,” 2018, [Online]. Available: https://www.deped.gov.ph/2018/10/03/deped-pnp-foil-illegal-online-sale-of-deped-issued-laptop-warns-of-legal-action-vs-theft-of-govt-property/
  • G. Wang, H. Ding, M. Duan, Y. Pu, Z. Yang, and H. Li, “Fighting against terrorism: A real-time CCTV autonomous weapons detection based on improved YOLO v4,” Digit. Signal Process., vol. 132, p. 103790, Dec. 2022, doi: 10.1016/j.dsp.2022.103790.
  • P. Siagian and E. Fernando, “The Design and Implementation of a Dashboard Web-Based Video Surveillance in OpenStack Swift,” Procedia Comput. Sci., vol. 179, pp. 448–457, 2021, doi: 10.1016/j.procs.2021.01.028.
  • V. Cho, K. C. Mansfield, and J. Claughton, “The past and future technology in classroom management and school discipline: A systematic review,” Teach. Teach. Educ., vol. 90, p. 103037, Apr. 2020, doi: 10.1016/j.tate.2020.103037.
  • M. D. Hernandez, A. C. Fajardo, R. P. Medina, J. T. Hernandez, and R. M. Dellosa, “Implementation of data augmentation in convolutional neural network and gradient boosted classifier for vehicle classification,” Int. J. Sci. Technol. Res., vol. 8, no. 12, pp. 185–189, 2019, [Online]. Available: http://www.ijstr.org/final-print/dec2019/Implementation-Of-Data-Augmentation-In-Convolutional-Neural-Network-And-Gradient-Boosted-Classifier-For-Vehicle-Classification.pdf
  • A. S. Alon, M. C. A. Venal, S. V. Militante, M. D. Hernandez, and H. B. Acla, “Lyco-frequency: A development of lycopersicon esculentum fruit classification for tomato catsup production using frequency sensing effect,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 4690–4695, 2020, doi: 10.30534/ijatcse/2020/72942020.
  • M. D. Hernandez, A. C. Fajardo, and R. P. Medina, “A Hybrid Convolutional Neural Network-Gradient Boosted Classifier for Vehicle Classification,” IJRTE J., no. 2, pp. 213–216, 2019, doi: 10.35940/ijrte.B1016.078219.
  • Cisco System, “The Internet of Everthings - Global Private Sector Economics Analysis,” Cisco Syst., pp. 1–9, 2013, [Online]. Available: https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy_FAQ.pdf
  • N. Di Cicco, F. Tonini, V. Cacchiani, and C. Raffaelli, “Optimization over time of reliable 5G-RAN with network function migrations,” Comput. Networks, vol. 215, p. 109216, Oct. 2022, doi: 10.1016/j.comnet.2022.109216.
  • C. H. Lee et al., “On natural language call routing,” Speech Commun., vol. 31, no. 4, pp. 309–320, 2000, doi: 10.1016/S0167-6393(99)00064-3.
  • T. D. Shashikala, S. L. Sunitha, and S. Basavarajappa, “Quantification of worn surface using digital image processing,” Tribol. Int., vol. 176, p. 107864, Dec. 2022, doi: 10.1016/j.triboint.2022.107864.
  • S. Sahoo, N. Dash, and P. Sahoo, “Word Extraction from Speech Recognition using Correlation Coefficients,” Int. J. Comput. Appl., vol. 51, no. 13, pp. 21–25, 2012, doi: 10.5120/8102-1694.
  • Himanshu Jain, D. Kroening, N. Sharygina, and E. Clarke, “Word level predicate abstraction and refinement for verifying RTL Verilog,” in Proceedings. 42nd Design Automation Conference, 2005., 2005, pp. 445–450. doi: 10.1109/DAC.2005.193850.
  • H. Li and Q. Liu, “Hardware Trojan detection acceleration based on word-level statistical properties management,” in 2014 International Conference on Field-Programmable Technology (FPT), Dec. 2014, pp. 153–160. doi: 10.1109/FPT.2014.7082769.
  • J. Yadav and K. S. Rao, “Generation of emotional speech by prosody imposition on sentence, word and syllable level fragments of neutral speech,” in 2015 International Conference on Cognitive Computing and Information Processing(CCIP), Mar. 2015, pp. 1–5. doi: 10.1109/CCIP.2015.7100694.
  • X. Sun, P. Kalla, and F. Enescu, “Word-level traversal of finite state machines using algebraic geometry,” in 2016 IEEE International High Level Design Validation and Test Workshop (HLDVT), Oct. 2016, pp. 142–149. doi: 10.1109/HLDVT.2016.7748268.
  • C. Yu and M. Ciesielski, “Automatic word-level abstraction of datapath,” in 2016 IEEE International Symposium on Circuits and Systems (ISCAS), May 2016, pp. 1718–1721. doi: 10.1109/ISCAS.2016.7538899.
  • A. Saepullah, “Comparative Analysis of Mamdani, Sugeno and Tsukamoto Method of Fuzzy Inference System for Air Conditioner Energy Saving,” J. Intell. Syst., vol. 1, no. 2, pp. 143–147, 2015.
  • OpenCV, “Template Matching,” 2019. https://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/template_matching/template_matching.html (accessed Mar. 10, 2023).
  • J. Xu and P. Mishra, “Combining deep learning with chemometrics when it is really needed: A case of real time object detection and spectral model application for spectral image processing,” Anal. Chim. Acta, vol. 1202, p. 339668, Apr. 2022, doi: 10.1016/j.aca.2022.339668.
  • T. Scharf, C. L. Kirkland, M. L. Daggitt, M. Barham, and V. Puzyrev, “AnalyZr: A Python application for zircon grain image segmentation and shape analysis,” Comput. Geosci., vol. 162, p. 105057, May 2022, doi: 10.1016/j.cageo.2022.105057.
  • T. Peng-o and P. Chaikan, “High performance and energy efficient sobel edge detection,” Microprocess. Microsyst., vol. 87, p. 104368, Nov. 2021, doi: 10.1016/j.micpro.2021.104368.
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