Kajian Literatur Metode Pendeteksian dan Pengenalan Kendaraan Berdasarkan Citra Digital
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Published:
Sep 12, 2020
Keywords:
Deteksi, Pengenalan, Kendaraan, Citra Digital
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Abstract
Pendeteksian dan pengenalan kendaraan menjadi topik yang menarik oleh para peneliti terutama di bidang visi komputer. Sistem pendeteksian dan pengenalan kendaraan secara otomatis dan real-time merupakan bagian penting pada Intelligent Transportation System (ITS). Pada makalah ini membahas beberapa kajian literatur tentang metode yang digunakan untuk pendeteksian dan pengenalan kendaraan. Kajian dilakukan dengan cara meninjau literatur yang berhubungan dengan pendeteksian dan pengenalan kendaraan menggunakan pendekatan image processing, baik dengan data masukan berupa citra maupun video. Hasil yang diharapkan dapat menjadi acuan untuk peneliti yang hendak melakukan penelitian tentang pendeteksian dan pengenalan kendaraan.
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Rani, S., & Amwin, A. (2020). Kajian Literatur Metode Pendeteksian dan Pengenalan Kendaraan Berdasarkan Citra Digital. PETIR, 13(2), 223–228. https://doi.org/10.33322/petir.v13i2.1026
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References
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[5] H. Wang, Y. Cai, X. Chen, and L. Chen, “Night-time vehicle sensing in far infrared image with deep learning,” J. Sensors, vol. 2016, 2016, doi: 10.1155/2016/3403451.
[6] E. Ul Haq, S. J. Hussain Pirzada, J. Piao, T. Yu, and H. Shin, “Image processing and vision techniques for smart vehicles,” ISCAS 2012 - 2012 IEEE Int. Symp. Circuits Syst., no. May, pp. 1211–1214, 2012, doi: 10.1109/ISCAS.2012.6271453.
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[8] M. Irfan, B. A. Ardi Sumbodo, and I. Candradewi, “Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 7, no. 2, p. 139, 2017, doi: 10.22146/ijeis.18260.
[9] F. Zhang, D. Clarke, and A. Knoll, “Vehicle detection based on LiDAR and camera fusion,” 2014 17th IEEE Int. Conf. Intell. Transp. Syst. ITSC 2014, pp. 1620–1625, 2014, doi: 10.1109/ITSC.2014.6957925.
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[13] W. Zhang, L. Chen, W. Gong, Z. Li, Q. Lu, and S. Yang, “An integrated approach for vehicle detection and type recognition,” Proc. - 2015 IEEE 12th Int. Conf. Ubiquitous Intell. Comput. 2015 IEEE 12th Int. Conf. Adv. Trust. Comput. 2015 IEEE 15th Int. Conf. Scalable Comput. Commun. 20, no. August, pp. 798–801, 2016, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.157.
[14] S. Zeng, X. Niu, and Y. Dou, “Vehicle recognition for surveillance video using sparse coding,” Commun. Comput. Inf. Sci., vol. 484, pp. 228–234, 2014, doi: 10.1007/978-3-662-45643-9_24.
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[16] R. Silva, K. Aires, R. Veras, T. Santos, K. Lima, and A. Soares, “Automatic Motorcycle Detection on Public Roads,” CLEI Electron. J., vol. 16, no. 3, pp. 1–9, 2013, doi: 10.19153/cleiej.16.3.4.
[17] R. Baran, A. Glowacz, and A. Matiolanski, “The efficient real- and non-real-time make and model recognition of cars,” Multimed. Tools Appl., vol. 74, no. 12, pp. 4269–4288, 2015, doi: 10.1007/s11042-013-1545-2.
[18] Y. Tang, C. Zhang, R. Gu, P. Li, and B. Yang, “Vehicle detection and recognition for intelligent traffic surveillance system,” Multimed. Tools Appl., vol. 76, no. 4, pp. 5817–5832, 2017, doi: 10.1007/s11042-015-2520-x.
[19] D. T. Munroe and M. G. Madden, “Multi-class and single-class classification approaches to vehicle model recognition from images,” 16th Irish Conf. Artif. Intell. Cogn. Sci., pp. 93–102, 2005, [Online]. Available: https://aran.library.nuigalway.ie/xmlui/handle/10379/191.