Segmentasi Kendaraan Menggunakan Improve Blob Analysis (BA) Pada Video Lalu Lintas

Penulis

  • Sutrisno .
  • Imam Cholissodin
  • Rina Christanti
  • Candra Dewi
  • Nurul Hidayat

DOI:

https://doi.org/10.25126/jtiik.201521132

Abstrak

Abstrak
Penggunaan citra digital untuk keperluan penelitian sudah banyak dilakukan, salah satunya yaitu segmentasi. Segmentasi berfungsi untuk mendeteksi objek - objek yang terdapat pada citra, sehingga hasil segmentasi sangat penting untuk proses selanjutnya. Pada penelitian ini diusulkan teknik optimasi hasil background subtraction menggunakan kombinasi frame difference (FD) atau difference image dengan filter SDGD dan running average (RA) atau background updating dengan filter SDGD untuk diterapkan pada blob analysis. Alasan utama menggunakan penggabungan kedua metode tersebut adalah karena seringnya terdapat piksel objek yang tidak mampu dideteksi sehingga akan mengurangi tingkat optimasi pengenalan objek. Hasil pengujian akurasi dari 10 data uji yang masing – masing terdiri dari 30 frame menunjukkan bahwa aplikasi ini memiliki nilai akurasi tertinggi yakni 90% untuk pengujian threshold dan 100% untuk pengujian ukuran structure element. Sehingga dapat disimpulkan bahwa aplikasi ini mampu melakukan segmentasi kendaraan dengan baik.
Kata kunci: filter SDGD, blob analysis, video lalu lintas, background subtraction.


Abstract
The use of digital images for the purposes of research has been often applied, one of them is segmentation. Segmentation is used to detect objects contained in the image, so the segmentation result is very important for further processing. In this study, the results of the optimization technique proposed background subtraction using a combination of frame difference (FD) or a difference image with filter SDGD and running average (RA) or background updating with SDGD filter to be applied blob analysis. The main reason to use the merger of these two methods is that often there are pixels that are not able to detect objects that will reduce the level of optimization object recognition. The results of accuracy testing using 10 data testing for each data consisting of 30 frames shows that the system proposed in this paper has best accuracy of 90% for testing the threshold and 100% for testing the size of structure element. So it can be concluded that this system capable to segmentation the vehicle properly.
Keywords: filter SDGD, blob analysis, traffic video, background subtraction

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Referensi

ANONYMOUS. 2011. Image processing fundamentals: derivative-based operation. http://www.mif.vu.lt/atpazinimas/dip/FIP/fip-Derivati.html.

EL-GLALY, Y. N. 2007. Development of PDE-Based Digital Inpainting Algorithm Applied to Missing Data in Digital Images [M.S. thesis]. Ain Shams University.

HAGARA, M & MORAVCIK, J. 2002. PLUS operator for edge detection in digital images. in Proceedings of the International Conference of Radioelektonika. 467–470.

LIN BO, ZHOU HEQIN. 2003. Using object classification to improve urban traffic monitoring system. IEEE, International Conference on Neural Networks and Signal Processing, Vol. 2. 1155-1159.

NARENDRA, V. G. & HAREESH, K. S. 2009. Study and comparison of various image edge detection techniques. International Journal of Image Processing, vol. 4, no. 2, article 83.

PERSOON, M. P., SERLIE, I. W. O., POST, F. H., TRUYEN, R., & VOS, F. M. 2003. Visualization of noisy and biased volume data using first and second order derivative techniques. in Proceedings of the 14th IEEE Visualization Conference. 379–385.

RAHMAN, F. Y. A. 2013. Enhancement of Background Subtraction Techniques Using a Second Derivative in Gradient Direction Filter. Hindawi Publishing Corporation, Journal of Electrical and Computer Engineering.

THOU-HO, YU-FENG, L, & TSONG-YI, C. 2007. Intelligent Vehicle Counting Method Based on Blob Analysis in Traffic Surveillance. IEEE, 0-7695-2882-1/07 $25.00 ©2007.

YOUNG, I. J., GERBRANDS, J.J., & VAN VLIET, L. J. 2007. Fundamentals of Image Processing. Version 2. 3.

YOUNG, I. T. 1996. Generalized convolutional filtering. in Proceedings of the 19th CERN School of Computing. 51–65.

VERBEEK, P. W. & VAN VLIET, L. J. 1994. Location error of curved edges in low-pass filtered 2-D and 3-D images. IEEE. Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 7. 726–733.

Unduhan

Diterbitkan

17-02-2015

Terbitan

Bagian

Teknologi Informasi

Cara Mengutip

Segmentasi Kendaraan Menggunakan Improve Blob Analysis (BA) Pada Video Lalu Lintas. (2015). Jurnal Teknologi Informasi Dan Ilmu Komputer, 2(1), 67-72. https://doi.org/10.25126/jtiik.201521132