Pendeteksian Kendaraan dengan Menggunakan Metode Running Average Background Substraction dan Morfologi Citra
DOI:
https://doi.org/10.30865/mib.v4i4.2315Keywords:
Running Average, Background Substraction, Morphological Operations, Centroid, TrafficAbstract
Traffic conditions on the highway at this time has started to be crowded. To find out if there is traffic jam or can not be seen by counting the number of vehicles passing through the area. However, it is impossible for security forces to count the number of vehicles manually. This requires a method that can be used and is calculated from the number of vehicles. In this study, the running average background substraction method and morphological operations were used to detect vehicles and use the center of the object (centroid) to calculate the number of vehicles. The sample used is a traffic video in the Bekasi area. From the research results, there were 37 vehicles detected in real conditions and stated as vehicles in the application and there were 7 vehicles detected in real conditions but not stated in the application. The next stage is an evaluation by calculating the value of precision, recall and accuracy. In this study, the precision value obtained was 84.09%, the recall value was 94.87% and the accuracy rate was 80.43%.
References
J. Park, A. Tabb, and A. C. Kak, “Hierarchical Data Structure for Real-Time Background Subtraction,†in Proceedings - International Conference on Image Processing, ICIP, 2006, pp. 1849–1852, doi: 10.1109/ICIP.2006.312840.
S. S. Babu, S. S. Babu, H. Khan, and M. K. Chowdary, “Implementation of Running Average Background Subtraction Algorithm in FPGA for Image Processing Applications,†Int. J. Comput. Appl., vol. 73, no. 21, pp. 41–46, 2013, doi: 10.5120/13022-0259.
B. Sharma, V. K. Katiyar, A. K. Gupta, and A. Singh, “The Automated Vehicle Detection of Highway Traffic Images by Differential Morphological Profile,†J. Transp. Technol., vol. 04, no. 02, pp. 150–156, 2014, doi: 10.4236/jtts.2014.42015.
M. Shehata, R. Abo-Al-Ez, F. Zaghlool, and M. Taha, “Vehicles Detection Based on Background Modeling,†Int. J. Eng. Trends Technol., vol. 66, no. 2, pp. 92–95, 2018, doi: 10.14445/22315381/ijett-v66p216.
A. V Meru and I. I. Mujawar, “Computer Vision Based Vehicles Detection and Traffic Control for Four Way Road,†Tec. Res. Organ. India, vol. 2, no. 6, pp. 20–25, 2015.
M. H. Sigari, N. Mozayani, and H. R. Pourreza, “Fuzzy Running Average and Fuzzy Background Subtraction: Concepts and Application,†IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 8, no. 2, pp. 138–143, 2008.
A. G. Rad, A. Dehghani, and M. R. Karim, “Vehicle Speed Detection in Video Image Sequences Using CVS Method,†Int. J. Phys. Sci., vol. 5, no. 17, pp. 2555–2563, 2010.
Q. Tian, L. Zhang, Y. Wei, W. Zhao, and W. Fei, “Vehicle Detection and Tracking at Night in Video Surveillance,†Int. J. Online Eng., vol. 9, no. 6, pp. 60–64, 2013, doi: 10.3991/ijoe.v9iS6.2828.
H. S. Sundoro and A. Harjoko, “Vehicle Counting and Vehicle Speed Measurement Based on Video Processing,†J. Theor. Appl. Inf. Technol., vol. 84, no. 2, pp. 233–241, 2016.
R. Kosasih, A. Fahrurozi, and I. Mardhiyah, “Vehicle Detection Using Principal Component Analysis,†J. Ilm. Komputasi, vol. 19, no. 2, pp. 155–160, 2020.
F. Y. Abdul Rahman, A. Hussain, W. M. D. Wan Zaki, H. Badioze Zaman, and N. Md Tahir, “Enhancement of Background Subtraction Techniques Using a Second Derivative in Gradient Direction Filter,†J. Electr. Comput. Eng., vol. 2013, pp. 1–12, 2013, doi: 10.1155/2013/598708.
A. Miranto, S. R. Sulistiyanti, and F. X. Arinto Setyawan, “Adaptive Background Subtraction for Monitoring System,†in 2019 International Conference on Information and Communications Technology, ICOIACT 2019, 2019, pp. 153–156, doi: 10.1109/ICOIACT46704.2019.8938501.
M. Goyal, “Morphological Image Processing,†Int. J. Comput. Sci. Technol., vol. 2, no. 4, pp. 161–165, 2011.
R. Kosasih, “Automatic Segmentation of Abdominal Aortic Aneurism ( AAA ) By Using Active Contour Models,†Sci. J. Informatics, vol. 7, no. 1, pp. 66–74, 2020.
D. P. Lestari, R. Kosasih, T. Handhika, Murni, I. Sari, and A. Fahrurozi, “Fire Hotspots Detection System on CCTV Videos Using You only Look Once (YOLO) Method and Tiny YOLO Model for High Buildings Evacuation,†in Proceedings - 2019 2nd International Conference of Computer and Informatics Engineering: Artificial Intelligence Roles in Industrial Revolution 4.0, IC2IE 2019, 2019, pp. 87–92, doi: 10.1109/IC2IE47452.2019.8940842.
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