DOI: https://doi.org/10.14710/jtsiskom.6.1.2018.32-36

Maximum Likelihood Classification dengan Ekstraksi Fitur Fast Fourier Transform untuk Pengenalan Mobil

Maximum Likelihood Classification with Fast Fourier Transform Feature Extraction for Car Recognition

Department of Informatics, STMIK Global Informatika MDP Palembang, Indonesia

Received: 3 Dec 2017; Published: 31 Jan 2018.
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

How to cite (IEEE): D. Alamsyah, "Maximum Likelihood Classification dengan Ekstraksi Fitur Fast Fourier Transform untuk Pengenalan Mobil," Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 1, pp. 32-36, Jan. 2018. https://doi.org/10.14710/jtsiskom.6.1.2018.32-36
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Abstract

The car recognition is part of the field of traffic surveillance on the image. In general, the car recognition using the form-based feature as a unique feature. Another feature in object recognition is the frequency feature. One feature of frequency is the Fourier feature, this feature is obtained by using Fast Fourier Transform (FFT) method. The object recognition can be done by determining the maximum value of likelihood and classifying it with Maximum Likelihood Classification (MLC). The use of FFT and MLC in the car object recognition has never been used. The results of both are in a good accuracy that is 76%.

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Keywords: Fast fourier transform; maximum likelihood classification; car recognition

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Section: Original Research Articles
Language : ID
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  1. M. Al-Smadi, K. Abdulrahim, and R. A. Salam, "Traffic surveillance: A review of vision based vehicle detection, recognition and tracking," Int. J. Appl. Eng. Res., vol. 11, no. 1, pp. 713–726, 2016
  2. Z. Wang, and K. Hong, "A new method for robust object tracking system based on scale invariant feature transform and camshift," in Proc. 2012 ACM Research in Applied Computation Symposium, Oct. 2012, pp. 132-136
  3. X. Chen, and Q. Meng, "Vehicle detection from UAVs by using SIFT with implicit shape model," in Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. 2013, pp. 3139-3144
  4. Z. Qian, J. Yang, and L. Duan, "Multiclass vehicle tracking based on local feature", in Proc. 2013 Chinese Conference on Intelligent Automation, Jan. 2013, pp. 137-144
  5. L. Wei, X. Xudong, W. Jianhua, Z. Yi, and H. Jianming, "A SIFT-based mean shift algorithm for moving vehicle tracking," in Proc. IEEE Intelligent Vehicles Symposium, Jun. 2014, pp. 762-767
  6. J. W. Hsieh, L. C. Chen, and D. Y. Chen, "Symmetrical SURF and its applications to vehicle detection and vehicle make and model recognition," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp. 6-20, 2014
  7. T. D. Gamage, J. G. Samarawickrama, and A. A. Pasqual, "GPU based non-overlapping multi-camera vehicle tracking," in IEEE 7th International Conference on Information and Automation for Sustainability (ICIAfS), Dec. 2014, pp. 1-6
  8. L. C. Chen, J. W. Hsieh, H. F. Chiang, and T. H. Tsai, "Real-time vehicle color identification using symmetrical SURFs and chromatic strength," in Proc. of IEEE International Symposium on Circuits and Systems, May. 2015, pp. 2804-2807
  9. B. F. Momin, and S. M. Kumbhare, "Vehicle detection in video surveillance system using Symmetrical SURF," in IEEE International Conference on Electrical, Computer and Communication Technologies(ICECCT), Mar. 2015, pp. 1-4
  10. M. Cheon, W. Lee, C. Yoon, and M. Park, "Vision-based vehicle detection system with consideration of the detecting location," IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 3, pp. 1243-1252, 2012
  11. S. Tuermer, F. Kurz, P. Reinartz, and U. Stilla, "Airborne vehicle detection in dense urban areas using HoG features and disparity maps," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 6, pp. 2327-2337, 2013
  12. H. Huijie, X. Chao, Z. Jun, and G. Wenjun, "The moving vehicle detection and tracking system based on video image," in Proc. IEEE 3ed International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC), Sep. 2013, pp. 1277-1280
  13. B. F. Wu, C. C. Kao, C. L. Jen, Y. F. Li, Y. H. Chen, and J. H. Juang, "A relative-discriminative-histogram-of-oriented-gradients-based particle filter approach to vehicle occlusion handling and tracking," IEEE Transactions on Industrial Electronics, vol. 61, no. 8, pp. 4228-4237, 2014
  14. D. Alamsyah, "Pengenalan Mobil pada Citra Digital Menggunakan HOG-SVM," Jatisi, vol. 1, no. 2, pp. 162-168, 2017
  15. T. T. Nguyen, and T. T. Nguyen, "A real time license plate detection system based on boosting learning algorithm," in Proc. IEEE 5th International Congress on Image and Signal Processing (CISP), Oct. 2012, pp. 819-823
  16. B. Zhang, and Y. Zhou, "Reliable vehicle type classification by classified vector quantization," in Proc. IEEE 5th International Congress on Image and Signal Processing (CISP), Oct. 2012, pp. 1148-1152
  17. S. M. Elkerdawi, R. Sayed, and M. ElHelw, "Real-time vehicle detection and tracking using Haar-like features and compressive tracking," in 1st Iberian Robotics Conference, Jan. 2014, pp. 381-390. Springer International Publishing
  18. S. El Kerdawy, A. Salaheldin, and M. ElHelw, "Vision-based scale-adaptive vehicle detection and tracking for intelligent traffic monitoring," in IEEE International Conference on Robotics and Biomimetics, Dec. 2014, pp.1044-1049
  19. N. Miller, M.A. Thomas, J. A. Eichel, and A. Mishra, "A hidden markov model for vehicle detection and counting," in IEEE 12th Conference on Computer and Robot Vision (CRV), Jun. 2015, pp. 269-276
  20. B. F. Momin, and T. M. Mujawar, "Vehicle detection and attribute based search of vehicles in video surveillance system," in IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT), Mar. 2015, pp. 1-4
  21. Y. Rangaswamy, K. B. Raja, and K. R. Venugopal, "FRDF: Face Recognition using Fusion of DTCWT and FFT Features." Procedia Computer Science. vol. 54. pp. 809-817, 2015
  22. D. Zhang, D. Ding. J. Li, and Q.Liu, "PCA Based Extracting Feature Using Fast Fourier Transform for Facial Expression Recognition," Transaction on Engineering Technologies, Springer, 2015. pp. 413-424
  23. G. Aguilar, G. Sanchez, K. Toscano, M. N. Miyatake, and H. P. Meana, "Automatic Fingerprint Recognition System Using Fast Fourier Transform and Gabor Filters," Cientifika, vol. 12, no. 1, pp. 9-16, 2008
  24. A. Ahmad. Analysis of Maximum Likelihood Classification of Multispectral Data. Applied Mathematical Sciences. 2012. Vol. 6. Pp. 6425-6436

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