Aplikasi Penghitung Kendaraan Pada Jalur Pantura Menggunakan Blob Deteksi Dan Kalman Filter
Abstract
Jalur puntara jawa merupakan jalan raya wilayah utara jawa membentang dari merak sampai ke banyuwangi. Jalur pantura ini merupakan jalur padat yang dilalui oleh kendaraan baik roda 4 maupun lebih. Beban jalan pantura per tahun mencapai 1 juta ton. Selain itu juga dengan beban ini merupakan penyumbang penggunaan BBM (bahan bakar minyak) yang lebih sebesar 42 ribu kiloliter dan mengakibatkan pemberi emisi udara cukup banyak sebesar 350 ton per hari. Dengan demikian semakin banyak kendaraan yang melintas jalur pantura, sehingga perlu diketahui tentang jumlah kendaraan yang melintas dijalur pantura secara otomatis oleh sistem. Data ini dapat dijadikan rujukan beban jalan pantura secara tepat. Pada penelitian ini, menggunakan beberapa algoritma dari blob detection dengan menggunakan library openCV dan kalman filter. Hasil dari deteksi dengan menggunakan bolb deteksi masih terdapat beberapa error yang cukup besar dan menggunakan kalman filter didapatkan 78.81 %.
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References
Z. Yuanyuan, Z. Kaiwen, and M. Yuming, “Research on Vehicle Detection Method Based on Video Image,” 2012 Int. Conf. Ind. Control Electron. Eng., pp. 987–990, 2012.
K. Cheng, C. Lin, Y. Chen, T. Su, S. Lai, and J. Lee, “Design of vehicle detection methods with OpenCL programming on multi-core systems,” 11th IEEE Symp. Embed. Syst. Real-time Multimed., pp. 88–95, 2013.
Seunghun Jin, Junguk Cho, Xuan Dai Pham, Kyoung Mu Lee, Sung-Kee Park, Munsang Kim, and Jae Wook Jeon, “FPGA Design and Implementation of a Real-Time Stereo Vision System,” IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 1, pp. 15–26, 2010.
L. C. Leon and R. Hirata, “Vehicle detection using mixture of deformable parts models: Static and dynamic camera,” Brazilian Symp. Comput. Graph. Image Process., pp. 237–244, 2012.
S. Kim, S. Oh, J. Kang, K. Kim, S. Park, and K. Park, “Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion,” 2005 IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 2173–2178, 2005.
B. N. Thanh and T. C. Sun, “An improved real-time blob detection for visual surveillance,” Proc. 2009 2nd Int. Congr. Image Signal Process. CISP’09, pp. 0–4, 2009.
J. Liu, J. M. White, and R. M. Summers, “Automated detection of blob structures by hessian analysis and object scale,” Proc. - Int. Conf. Image Process. ICIP, pp. 841–844, 2010.
M. Harmouchi, “Computer blob detection and tracking for highly repeatable optical fiber sensor.”
D. Kiran and A. I. Rasheed, “FPGA Implementation of Blob Detection Algorithm for Object Detection in Visual Navigation.”
_____,Tersedia pada http://www.tempo.co/read/news/2014/03/29/090566333/Rel-Ganda-Kurangi-30-Persen-Beban-Jalur-Pantura diakses 6 April 2015
Welch, G. B. (2001). An Introduction to the Kalman Filter. SIGGRAPH .
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