Warning Traffic System

Wanchalrm Chaithip, Supasin Nguantad, Sarun Olankranok, Sirikan Chucherd


Nowadays, many people use the road to travel daily. Resulting in many types of vehicles on the road since we have to share the road with many people how can we be sure that it is safe? Traffic conditions and vehicle driver decisions are also one of the factors in accidents. Therefore, we decided to build a device that helps us to know the current traffic conditions and is aiding in our road usage decisions. Our equipment will consist of a device for vehicle detection, a camera for detecting all vehicles, and a Raspberry Pi used for processing the number of vehicles entered and displayed via a display. Consisting of LED. If there are a lot of vehicles, there will be a warning to road users on our display. We test our system by looking at traffic conditions at different times. Shows the times when there is bad traffic. It was created to help make decisions for road users.

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Sumardi, S., Taufiqurrahman, M., & Riyadi, M. A. (2018). Street Mark Detection Using Raspberry PI for Self-Driving System. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16(2),629.

doi: 10.12928/telkomnika.v16i1.4509

Hillel, A. B., Lerner, R., Levi, D., & Raz, G. (2012). Recent progress in road and lane detection: a survey. Machine Vision and Applications, 25(3), 727–745. doi: 10.1007/s00138-011-0404-2

Arruda, V. F., Paixao, T. M., Berriel, R. F., Souza, A.F.D., Badue, C., Sebe, N., & Oliveira-Santos, T. (2019). Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night. 2019 International Joint Conference on Neural Networks (IJCNN).

doi: 10.1109/ijcnn.2019.8852008

Yu, S., & Deng, S. (2018). Adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm. EURASIP Journal on Image and Video Processing, 2018(1). doi: 10.1186/s13640- 018-0381-8

Motion Detection and Face Recognition using Raspberry Pi, as a Part of, the Internet of Things. (2019). Acta Polytechnica Hungarica, 16(3).

doi: 10.12700/aph.16.3.2019.3.9

Bradski, G. R., & Kaehler, A. (2011). Learning OpenCV: Beijing: OReilly.

Digital image processing. (2019, November 14)


Erdemarslan/opencv_motion.py. (2018). Retrieved from



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