Vehicle Counter in Traffic Using Pixel Area Method with Multi-Region of Interest

Moch Fachri, Nur Hikmah, Nuke Lu'lu ul Chusna

Abstract


Traffic density data plays an important role in decision making by the Intelligent Transportation System (ITS). This system uses this data in the process of adaptive traffic management. The inaccuracy of the data provided into the ITS system can result in errors in decision making. This study utilizes digital image engineering technology in the detection of four-wheeled vehicles in traffic traffic for the purpose of acquiring traffic density data. In this study, we propose a multi-ROI (Region of Interest) pixel area method. This multi-ROI proposal is to be put forward to improve reading accuracy compared to just one ROI. With the use of this multi-ROI, the information obtained from the overall ROI can strengthen the accuracy of the data of vehicles passing in a lane. Our experimental results show that the use of multi-ROI with a certain amount of ROI can produce an accuracy rate of up to 88.66% compared to single-ROI which has an accuracy rate of 84.65%.


Keywords


vehicle detection; pixel area; traffic counter; digital image engineering

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References


A. El Bouziady, ROH Thami, M. Ghogho, O. Bourja, S. El Fkihi, “Vehicle speed estimation using extracted SURF features from stereo images,” in 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Apr. 2018, pp. 1–6, doi:10.109/ISACV.2018.8354040.

AAAR Magableh, MA Almakhadmeh, N. Alshin, and AF Klaib, “Smart Traffic Light Management Systems: A Systematic Literature Review,” International Journal of Technology Diffusion, vol. 11, no. 3, pp. 22–47, Jul. 2020, doi: 10.4018/IJTD.2020070102.

Agussani. (2020). Implementation Analysis of Trans Mebidang Program in Transportation Mode Services, North Sumatera (Comperative Study of Medan City). Budapest International Research and Critics Institute-Journal (BIRCI-Journal). P. 1167-1174.

AMAM Akbar, A. Kurniawan, A. Zaini, “Traffic IP Camera for Counting Four-Wheel Vehicles Using the Pixel Area Method,” in the Digital Library of the Sepuluh Nopember Institute of Technology, Surabaya, Apr. 2016.

C. Vertan, C. Florea, L. Florea, MS Badea, “Reusing the Otsu threshold beyond segmentation,” in 2017 International Symposium on Signals, Circuits and Systems (ISSCS), Iasi, Romania, Jul. 2017, pp. 1–4, doi:10.109/ISSCS.2017.8034908.

D. Pavleski, D. Koltovska Nechoska, “Evaluation of Adaptive and Fixed Time Traffic Signal Strategies: Case study of skopje,” Transport for Today's Society, 2019, doi: 10.20544/TTS2018.P21.

Drive Cam UK, Aug. 04, 2020. https://sites.google.com/site/drivecamuk/.

E. Odat, JS Shamma, C. Claudel, “Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors,” IEEE Trans. Intell. Transport. Syst., vol. 19, no. 5, pp. 1593–1606, May 2018, doi: 10.1109/TITS.2017.2727224.

M. Al-Smadi, K. Abdulrahim, RA Salam, “Traffic Surveillance: A Review of Vision Based Vehicle Detection, Recognition and Tracking,” vol. 11, no. 1, p. 15, 2016.

M. Liepins, A. Severdaks, “Vehicle detection using non-invasive magnetic wireless sensor network,” in 2013 21st Telecommunications Forum Telfor (TELFOR), Belgrade, Serbia, Nov. 2013, pp. 601–604, doi:10.109/TELFOR.2013.6716302.

MA Manzoor, Y. Morgan, “Vehicle Make and Model classification system using bag of SIFT features,” in 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, Jan. 2017, pp. 1–5, doi:10.109/CCWC.2017.7868475.

Open CV, I., 2017. Open source computer vision library.

P Jing, H Huang, L Chen, “An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review,” Information, vol. 8, no. 3, p. 101, Aug. 2017, doi:10.3390/info8030101.

P. McGowen and M. Sanderson, “Accuracy of Pneumatic Road Tube Counters,” Western District Annual Meeting Institute of Transportation Engineers, Anchorage AK, United States. p. 17. May. 2011

S. Bougharriou, F. Hamdaoui, A. Mtibaa, “Linear SVM classifier based HOG car detection,” in 2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Monastir, Dec. 2017, pp. 241–245, doi:10.109/STA.2017.8314922.

S. Dutt, A. Kalra, “A scalable and robust framework for intelligent real-time video surveillance,” in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, Sep. 2016, pp. 212–215, doi:10.109/ICACCI.2016.7732049.

S. Sheik Mohammed Ali, B. George, L. Vanajakshi, J. Venkatraman, “A Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic,” IEEE Trans. instrument. Meas., vol. 61, no. 5, pp. 1353–1360, May 2012, doi: 10.1109/TIM.2011.2175037.

SA Rajab, A. Mayeli, HH Refai, “Vehicle classification and accurate speed calculation using multi-element piezoelectric sensor,” in 2014 IEEE Intelligent Vehicles Symposium Proceedings, MI, USA, June. 2014, pp. 894–899, doi:10.109/IVS.2014.6856432.

Y. Na, Y. Guo, Q. Fu, Y. Yan, “An Acoustic Traffic Monitoring System: Design and Implementation,” in 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), Beijing, Aug. 2015, pp. 119–126, doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.41.

YB Brahme, PS Kulkarni, “An Implementation of Moving Object Detection, Tracking and Counting Objects for Traffic Surveillance System,” in 2011 International Conference on Computational Intelligence and Communication Networks, Gwalior, Oct. 2011, pp. 143–148, doi:10.109/CICN.2011.28.

Z. Yang, LSC Pun-Cheng, “Vehicle detection in intelligent transportation systems and its applications under varying environments: A review,” Image and Vision Computing, vol. 69, pp. 143–154, Jan. 2018, doi:10.1016/j.imavis.2017.09.008.




DOI: https://doi.org/10.33258/birci.v5i1.4067

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.