DOI: https://doi.org/10.21456/vol9iss2pp141-148

Deteksi Objek Terapung pada Sungai Martapura dengan Metode Haar Like Feature Menggunakan Kamera Smart Phone

*Nahdi Saubari  -  Universitas Muhammadiyah Banjarmasin, Indonesia
Rudy Ansari  -  Universitas Muhammadiyah Banjarmasin, Indonesia
Mukhaimy Gazali  -  Universitas Muhammadiyah Banjarmasin, Indonesia
Open Access Copyright (c) 2019 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract
Martapura river is the center of Banjarmasin’s local life, especially for water transportation and its famous floating market tourism spot. Due to various floating objects in Martapura river, a method to detect those objects is needed to control the condition of the river. In general, there are several methods to detect objects such as Gaussian, Support Vector Machine (SVM), Independent Component Analysis (ICA) and the newest method called Haar Like Feature (HLF). Those first three methods often used to detect moving object, while HLF mostly used to detect human’s face. This research aimed to examine the use of HLF method to detect floating objects in Martapura river by using smartphone’s camera with the specification of 16Megapixel and 1080p resolution. The data collected with random sampling technique in two different spots in Banjarmasin at different times. Images and videos then examined using HLF method. The result shows that HLF method by using smartphone camera cannot be used to identify any floating objec

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Keywords: Haar Like Feature (HLF); Object Detection; Floating; River; Smart Phone
Funding: Kementerian Riset, Teknologi, dan Pendidikan Tinggi Republik Indonesia

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Section: Research Articles
Language : EN
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  2. Athira, A.P., Vijayan, M., & Mohan, R., 2018. Industry Interactive Innovations in Science, Engineering and Technology. 11: p.367–376. Available at: http://link.springer.com/ 10.1007/978-981-10-3953-9
  3. Bencheriet, C.E., 2018. New face features to detect multiple faces in complex background. Evolving Systems 0(0): p.0. Available at: http://dx.doi.org/10.1007/s12530-017-9211-y
  4. Borghgraef, A., Barnich, O., Lapierre, F., Droogenbroeck, Philips, W., Acheroy, M., 2010. An evaluation of pixel-based methods for the detection of floating objects on the sea surface. Eurasip Journal on Advances in Signal Processing 2010(May 2014)
  5. Déniz, O., Castrillón, M., and Hernández, M., 2003. Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters 24(13): p.2153–2157
  6. Deshpande, A., Dashpute, P., Chaudhary, S., and Wankhade, S.B. 2016. Face Detection for Authentication using Haar Classifiers. 6(2): p.527–529
  7. Hinojosa, I.A., Rivadeneira, M.M. and Thiel, M., 2011. Temporal and spatial distribution of floating objects in coastal waters of central-southern Chile and Patagonian fjords. Continental Shelf Research 31(3–4): p.172–186
  8. Hiromoto, M., Nakahara, K., Sugano, H., Nakamura, Y. and Miyamoto, R., 2007. A specialized processor suitable for AdaBoost-based detection with haar-like features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  9. Al Jarouf, Y.A., and Kurdy, M.B., 2018. A Hybrid Method to Detect and Verify Vehicle Crash with Haar-Like Features and SVM over the Web. 2018 International Conference on Computer and Applications, ICCA 2018: p.177–182
  10. Komorkiewicz, M., Kluczewski, M. and Gorgon, M. 2012. Floating point HOG implementation for real-time multiple object detection. Proceedings - 22nd International Conference on Field Programmable Logic and Applications, FPL 2012: p.711–714
  11. Michael, A., 2018. Pengenalan Plat Kendaraan Berbasis Android menggunakan Viola Jones dan Kohonen Neural Network. ILKOM Jurnal Ilmiah 8(2): p.95
  12. Moghimi, M.M., Nayeri, M., Pourahmadi, M. and Moghimi, M.K., 2018. Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos. Available at: http://arxiv.org/abs/1801.01698
  13. Naba, A., Pratama, B.M., Nadhir, A. and Harsono, H. 2017. Haar-like feature based real-time neuro car detection system. Proceeding-2016 International Seminar on Sensors, Instrumentation, Measurement and Metrology, ISSIMM 2016: p.67–70
  14. Rochgiyanti, 2011. Fungsi sungai bagi masyarakat di Tepian Sungai Kuin Kota Banjarmasin. Jurnal Komunitas 3(1): p.51–59
  15. Sabuari, N., Isnanto, R. and Adi, K. 2016. Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah. Jurnal Sistem Informasi Bisnis 6(1): p.30
  16. Uddin, M.S. and Akhi, A.Y. 2015. Horse Detection Using Haar Like Features. International Journal of Computer Theory and Engineering 8(5): p.415–418
  17. Viola, P. and Jones, M. 2005. Rapid object detection using a boosted cascade of simple features. In Computer Society Conference on Computer Vision and Pattern Recognition, I-511-I-518
  18. Waliulu, R.F., 2018. Deteksi dan Penggolongan Kendaraan dengan Kalman Filter dan Model Gaussian di Jalan Tol. Jurnal Sistem Informasi Bisnis 8(1): p.1
  19. Zhu, S.,Gao, X., Wau, H.,Xu, G., Xie., Q., Yang, S., 2018. Moving object real-time detection and tracking method based on improved Gaussian mixture model. Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018 (1): p.654–658

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