Vehicle Classification using Haar Cascade Classifier Method in Traffic Surveillance System
Corresponding Author(s) : Agus Eko Minarno
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol 3, No 1, February-2018
Abstract
Object detection based on digital image processing on vehicles is very important for establishing monitoring system or as alternative method to collect statistic data to make efficient traffic engineering decision. A vehicle counter program based on traffic video feed for specific type of vehicle using Haar Cascade Classifier was made as the output of this research. Firstly, Haar-like feature was used to present visual shape of vehicle, and AdaBoost machine learning algorithm was also employed to make a strong classifier by combining specific classifier into a cascade filter to quickly remove background regions of an image. At the testing section, the output was tested over 8 realistic video data and achieved high accuracy. The result was set 1 as the biggest value for recall and precision, 0.986 as the average value for recall and 0.978 as the average value for precision.
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- Kementerian Perhubungan, Statistik Perhubungan volume l. 2016.
- Badan Pusat Statistik Provinsi DKI Jakarta, Statistik Transportasi DKI Jakarta 2015. 2015.
- N. Redhantika, “Kepadatan lalu lintas di kota malang,” Univ. Merdeka Malang, pp. 1–10, 2014.
- R. Anwar, “Menentukan Nilai Satuan Mobil Penumpang Kendaraan Di Kotamadya Banjarmasin,” vol. 1, no. 1, pp. 22–27, 2000.
- Fajar Mit Cahyana, “Perancangan Program Penghitung Jumlah Kendaraan Satu Arah Menggunakan Bahasa Pemograman C++ dengan Pustaka OpenCV,” Univ. Brawijaya, 2014.
- A. Helmi, “Apikasi Deteksi Tingkat Kepadatan Lalu Lintas Berdasarkan Jumlah Kendaraan Yang Lewat Menggunakan OpenCV,” 2015.
- C.-J. Lee, “obstacle detetction and avoidance via cascade classifier for wheeled mobile robot,” Int. Conf. Mach. Learn. Cybern., p. 5, 2015.
- M. Syarif, P. Studi, T. Informatika, F. I. Komputer, U. Dian, and N. Semarang, “Deteksi Kedipan Mata Dengan Haar Cascade Classifier Dan Contour Untuk Password Login,” Techno.com, vol. 14, no. 4, pp. 242–249, 2015.
- P. Viola and M. M. J. Jones, “Robust Real-Time Face Detection,” Int. J. Comput. Vis., vol. 57, no. 2, pp. 137–154, 2004.
- A. Mordvintsev, “OpenCV-Python Tutorials Documentation Release beta,” 2017.
- J. Howse, OpenCV Computer Vision with Python. 2013.
References
Kementerian Perhubungan, Statistik Perhubungan volume l. 2016.
Badan Pusat Statistik Provinsi DKI Jakarta, Statistik Transportasi DKI Jakarta 2015. 2015.
N. Redhantika, “Kepadatan lalu lintas di kota malang,” Univ. Merdeka Malang, pp. 1–10, 2014.
R. Anwar, “Menentukan Nilai Satuan Mobil Penumpang Kendaraan Di Kotamadya Banjarmasin,” vol. 1, no. 1, pp. 22–27, 2000.
Fajar Mit Cahyana, “Perancangan Program Penghitung Jumlah Kendaraan Satu Arah Menggunakan Bahasa Pemograman C++ dengan Pustaka OpenCV,” Univ. Brawijaya, 2014.
A. Helmi, “Apikasi Deteksi Tingkat Kepadatan Lalu Lintas Berdasarkan Jumlah Kendaraan Yang Lewat Menggunakan OpenCV,” 2015.
C.-J. Lee, “obstacle detetction and avoidance via cascade classifier for wheeled mobile robot,” Int. Conf. Mach. Learn. Cybern., p. 5, 2015.
M. Syarif, P. Studi, T. Informatika, F. I. Komputer, U. Dian, and N. Semarang, “Deteksi Kedipan Mata Dengan Haar Cascade Classifier Dan Contour Untuk Password Login,” Techno.com, vol. 14, no. 4, pp. 242–249, 2015.
P. Viola and M. M. J. Jones, “Robust Real-Time Face Detection,” Int. J. Comput. Vis., vol. 57, no. 2, pp. 137–154, 2004.
A. Mordvintsev, “OpenCV-Python Tutorials Documentation Release beta,” 2017.
J. Howse, OpenCV Computer Vision with Python. 2013.