Traffic Light Signal Detector using Average Light Intensity Method

 Benediktus Anindito (Universitas Narotama, Surabaya, Indonesia)
 Slamet Winardi (Universitas Narotama, Surabaya, Indonesia)
 (*)Moh Noor Al-Azam Mail (Universitas Narotama, Surabaya, Indonesia)

(*) Corresponding Author

Submitted: April 16, 2020; Published: July 20, 2020



Electronic Traffic Law Enforcement (ETLE) is a way of using information technology to record a violation of traffic. This ETLE was developed to support security, order, and safety in traffic. Some cities or districts in Indonesia have started to apply this ETLE in several locations, which usually have traffic lights and frequent violations at these locations. In this paper, one of the elements in ETLE is tested, which is a traffic light signal detector, which will be used as a basis for whether a vehicle violates a traffic light or not. This detector uses a CCTV camera mounted on the location. It then analyzed the intensity of several image areas on the traffic lights in red, yellow, and green. From the test results, this method can determine the conditions of the traffic lights with 100% accuracy


Computer Vision, Python, OpenCV, ETLE, eTilang

Full Text:


Article Metrics

Abstract View: 168 times | PDF View: 57 times


“Undang-Undang Republik Indonesia nomor 22 tahun 2009 tentang Lalu Lintas dan Angkutan Jalan.” Republik Indonesia, Jun. 22, 2009, Accessed: Mar. 10, 2020. [Online]. Available:

Ardito Ramadhan, “Polri Sebut Jumlah Kecelakaan Lalu Lintas Meningkat pada 2019,” Kompas Cyber Media, Dec. 28, 2019.

Muhammad Azizirrahman, Ellyn Normelani, and Deasy Arisanty, “Faktor Penyebab Terjadinya Kecelakaan Lalu Lintas pada Daerah Rawan Kecelakaan di Kecamatan Banjarmasin Tengah Kota Banjarmasin,” JPG, vol. 2, no. 3, pp. 20–37, May 2015, [Online]. Available:

E. Purwaningsih, “Analisis Kecelakaan Berlalu Lintas di Kota Jakarta dengan Menggunakan Metode K-Means,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 5, no. 1, pp. 139–144, Aug. 2019, doi: 10.33480/jitk.v5i1.712.

H. Schulzrinne, A. Rao, and R. Lanphier, “Real Time Streaming Protocol (RTSP),” RFC Editor, RFC2326, Apr. 1998. doi: 10.17487/rfc2326.

ITU-T, “ITU-T Recommendation H.264: Advanced video coding for generic audiovisual services.” International Telecommunication Union (ITU), Nov. 22, 2007, Accessed: Mar. 11, 2020. [Online]. Available:

M. N. Al-Azam, D. Rizaludin, Y. S. Raharjo, and A. Nugroho, “Message Queuing Telemetry Transport dalam Internet of Things menggunakan ESP-32,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 3, no. 3, p. 159, Jul. 2019, doi: 10.30865/mib.v3i3.1160.

G. C. Hillar, Internet of Things with Python: Interact with the world and rapidly prototype IoT applications using Python. Birmingham Mumbai: Packt Publishing Limited, 2016.

Adrian Rosebrock, Practical Python and OpenCV: An Introductory, Example Driven Guide to Image Processing and Computer Vision, 4th ed. Baltimore, MD:, 2019.

G. Cattaneo, A. Bruno, and F. Petagna, “H-264/RTSP Multicast Stream Integrity,” in Image Analysis and Processing - ICIAP 2017, vol. 10485, S. Battiato, G. Gallo, R. Schettini, and F. Stanco, Eds. Cham: Springer International Publishing, 2017, pp. 558–568.

Audio-Video Transport Working Group, H. Schulzrinne, S. Casner, R. Frederick, and V. Jacobson, “RTP: A Transport Protocol for Real-Time Applications,” RFC Editor, RFC1889, Jan. 1996. doi: 10.17487/rfc1889.

S. Casner, “Session Description Protocol (SDP) Bandwidth Modifiers for RTP Control Protocol (RTCP) Bandwidth,” RFC Editor, RFC3556, Jul. 2003. doi: 10.17487/rfc3556.

A. G. Sooai, A. Nugroho, M. N. A. Azam, S. Sumpeno, and M. H. Purnomo, “Virtual artifact: Enhancing museum exhibit using 3D virtual reality,” Dec. 2017, pp. 1–5, doi: 10.23919/TRONSHOW.2017.8275078.

B. Anindito, A. G. Sooai, M. M. Achlaq, M. N. Al-Azam, A. Winaya, and M. Maftuchah, “Indoor Agriculture: Measurement of The Intensity of LED for Optimum Photosynthetic Recovery,” in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Malang, Indonesia, Oct. 2018, pp. 356–361, doi: 10.1109/EECSI.2018.8752827.

Adrian Rosebrock, PhD, Dave Hoffman, MSc, David McDuffee, Abhishek Thanki, and Sayak Paul, Raspberry Pi for Computer Vision: Hacker Bundle - v1.0.1. Baltimore, MD:, 2019.

Adrian Rosebrock, PhD, Dave Hoffman, MSc, David McDuffee, Abhishek Thanki, and Sayak Paul, Raspberry Pi for Computer Vision: Hobbyist Bundle - v1.0.1. Baltimore, MD:, 2019.

R. Laganière, OpenCV computer vision application programming cookbook: over 50 recipes to help build computer vision applications in C++ using the OpenCV library, 2. ed. Birmingham: Packt Publ, 2014.

G. R. Bradski and A. Kaehler, Learning OpenCV: computer vision with the OpenCV library, 1. ed., [Nachdr.]. Beijing: O’Reilly, 2011.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Traffic Light Signal Detector using Average Light Intensity Method


  • There are currently no refbacks.


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

STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email :

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