Pendeteksian Api pada Video Menggunakan Wavelet Dan LBP

Penulis

  • Naufal Luthfi Saputra Telkom University
  • Febryanti Sthevanie Telkom University
  • Kurniawan Nur Ramadhani Telkom University

Abstrak

Abstrak-Keakuratan deteksi api selalu menjadi tujuan utama dalam penelitian deteksi api. Pemilihan metode yang digunakan dalam melakukan pendeteksian api merupakan hal yang paling mempengaruhi nilai akurasi dalam deteksi api. Penulis menggunakan metode rule based untuk memfilter nilai pixel api, lalu digunakan metode spatial analisis untuk mendapatkan nilai energi dari objek api dan menggunakan Local Binary Pattern sebagai texture analysis dan Support Vector Machine sebagai classifier dan didapatkan akurasi 89,35%.

Kata kunci-deteksi api, rule based, spatial analisis , wavelet transform, Local Binary Pattern, Support Vector Machine

Referensi

Hu, G. L., & Jiang, X. Early Fire Detection of Large Space Combining Thresholding with Edge Detection Techniques. Applied Mechanics and Materials, 44-47, 2060–2064, (2010).

Norsyahirah Izzati binti Zaidi, Nor Anis Aneza binti Lokman, Mohd Razali bin Daud, Hendriyawan Achmad and Khor Ai Chia. FIRE RECOGNITION USING RGB AND YCBCR COLOR SPACE VOL. 10, NO. 21, ISSN 1819-6608, (2015).

Jiao, Z., Zhang, Y., Xin, J., Yi, Y., Liu, D., & Liu, H. Forest Fire Detection with Color Features and Wavelet Analysis Based on Aerial Imagery. Chinese Automation Congress (CAC), (2018).

Olivares-Mercado, J., Toscano-Medina, K., Sánchez-Perez, G., Hernandez-Suarez, A., Perez-Meana, H., Sandoval Orozco, A. L., & García Villalba, L. J. Early Fire Detection on Video Using LBP and Spread Ascending of Smoke. Sustainability, 11(12), 3261, (2019).

Duong, H. D., & Tinh, D. T. An efficient method for vision-based fire detection using SVM classification. 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR), (2013).

Hu, W., Tan, T., Wang, L., & Maybank, S. A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 34(3), 334–352, (2004).

Bahadir Karasulu and Serdar Korukoglu. Performance Evaluation Software: Moving Object Detection and Tracking in Videos. [Online]. Available: http://link.springer.com/book/10.1007%2F978-1- 4614-6534-8 , (2013).

Dipali Shahare and Ranjana Shende, “Moving Object Detection with Fixed Camera and Moving Camera for Automated Video Analysis,” International Journal of Computer Applications Technology and Research, vol. 3, issue 5, pp. 277-283, (2014).

Poynton, C. A. A Guided Tour of Colour Space. New Foundation for Video Technology: The SMPTE Advanced Television and Electronic Imaging Conference, (1995).

Paschos, G. Perceptually uniform color spaces for color texture analysis: an empirical evaluation. IEEE Transactions on Image Processing, 10(6), 932–937, (2001).

Gupta, A., Bokde, N., Marathe, D., … Kishore. A Novel approach for Video based Fire Detection system using Spatial and Texture analysis. Indian Journal of Science and Technology, 11(19), 1–17, (2018).

Nyma, A., Kang, M., Kwon, Y.-K., Kim, C.-H., & Kim, J.-M. A Hybrid Technique for Medical Image Segmentation. Journal of Biomedicine and Biotechnology, 2012, 1–7, (2012).

R. Bogush, S. Maltsev, A. Aniskovich, Object Detection Using Wavelet Transform, 2005.

Kurniawardhani, A., Suciati, N., & Arieshanti, I. Klasifikasi Citra Batik Menggunakan Metode Ekstraksi Ciri Yang Invariant Terhadap Rotasi. JUTI: Jurnal Ilmiah Teknologi Informasi, 12(2), 48-60, (2014).

T. Ojala, M. Pietikainen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," in Pattern Recognition, vol. 29, no. 1, pp. 51 - 59, (1996).

Bahal, Bhiwani, Haryana, “Data Classification Using Support VectorMachine,” Journal of Theoretical and Applied Information Technology, (2009).

##submission.downloads##

Diterbitkan

2023-06-27

Terbitan

Bagian

Program Studi S1 Informatika