Identify Traffic Flow Volume using Image Processing for Intelligent Transport System Identifikasi Volume Arus Lalu Lintas Menggunakan Pengolahan Citra untuk Sistem Transportasi Cerdas
Main Article Content
Abstract
Detection of traffic flow volume is needed to get information about the number of vehicles on a street section. This information is required for traffic control. This study uses video data taken from four adjution street section in Manado City. Video data are processed using the background subtraction method in the segmentation process. Then morphological operation techniques are applied to improve segmentation results. The binarization method with a threshold value of 220 to eliminate shadows on vehicle objects. Vehicle shadows must be removed because it can reduce system accuracy especially if vehicle shadows are too large and connected to other vehicles. Accuracy results from vehicle detection are 95.04% for the Toar street, 94.62% for the Diponegoro street, 91.47% for the Lumimuut street and 95.18% for the 14 Februari street. Vehicle detection results will be calculated to get the number of vehicles then divided by the duration of observation time to get the traffic flow volume. The results of the traffic flow volume information are expected to be implemented on Smart Traffic Light.
Article Details
References
[2] I. Amirullah, “An intelligent traffic light system for reducing number of queuing cars in complex road junction | Request PDF.” Accessed: Mar. 07, 2025. [Online]. Available: https://www.researchgate.net/publication/317742207_An_intelligent_traffic_light_system_for_reducing_number_of_queuing_cars_in_complex_road_junction
[3] A. Chabchoub, “(PDF) Intelligent Traffic Light Controller using Fuzzy Logic and Image Processing,” ResearchGate, Oct. 2024, doi: 10.14569/IJACSA.2021.0120450.
[4] B. Ghazal, K. ElKhatib, K. Chahine, and M. Kherfan, “Smart traffic light control system,” in 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA), Apr. 2016, pp. 140–145. doi: 10.1109/EECEA.2016.7470780.
[5] Basri, Indrabayu, and A. Achmad, “Gaussian Mixture Models optimization for counting the numbers of vehicle by adjusting the Region of Interest under heavy traffic condition,” in 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), May 2015, pp. 245–250. doi: 10.1109/ISITIA.2015.7219986.
[6] A. Dubey, Akshdeep, and S. Rane, “Implementation of an intelligent traffic control system and real time traffic statistics broadcasting,” in 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Apr. 2017, pp. 33–37. doi: 10.1109/ICECA.2017.8212827.
[7] C. Saravanan, “Color Image to Grayscale Image Conversion,” in 2010 Second International Conference on Computer Engineering and Applications, Mar. 2010, pp. 196–199. doi: 10.1109/ICCEA.2010.192.
[8] W. Jatmiko et al., Integrated Traffic Intelligent System. 2014.
[9] N. A. Mandellos, I. Keramitsoglou, and C. T. Kiranoudis, “A background subtraction algorithm for detecting and tracking vehicles,” Expert Syst. Appl., vol. 38, no. 3, pp. 1619–1631, Mar. 2011, doi: 10.1016/j.eswa.2010.07.083.
[10] C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), Jun. 1999, pp. 246-252 Vol. 2. doi: 10.1109/CVPR.1999.784637.
[11] “Morphological Operations - an overview | ScienceDirect Topics.” Accessed: Jul. 07, 2022. [Online]. Available: https://www.sciencedirect.com/topics/engineering/morphological-operations
[12] A. Ajmal and I. M. Hussain, “Vehicle detection using morphological image processing technique,” in 2010 International Conference on Multimedia Computing and Information Technology (MCIT), Mar. 2010, pp. 65–68. doi: 10.1109/MCIT.2010.5444851.
[13] E. N. Julianto, “HUBUNGAN ANTARA KECEPATAN, VOLUME DAN KEPADATAN LALU LINTAS RUAS JALAN SILIWANGI SEMARANG,” J. Tek. Sipil Dan Perenc., vol. 12, no. 2, 2010, doi: 10.15294/jtsp.v12i2.1348.
[14] Y. Xu, G. Yu, X. Wu, Y. Wang, and Y. Ma, “An Enhanced Viola-Jones Vehicle Detection Method From Unmanned Aerial Vehicles Imagery,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 7, pp. 1845–1856, Jul. 2017, doi: 10.1109/TITS.2016.2617202.
[15] B. Pawar, V. T. Humbe, and L. Kundnani, “Morphology based moving vehicle detection,” in 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Mar. 2017, pp. 217–223. doi: 10.1109/ICBDACI.2017.8070837.