{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:24:24Z","timestamp":1772645064169,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>LEDs are widely employed as traffic lights. Because most LED traffic lights are driven by alternative power, they blink at high frequencies, even at twice their frequencies. We propose a method to detect a traffic light from images captured by a high-speed camera that can recognize a blinking traffic light. This technique is robust under various illuminations because it can detect traffic lights by extracting information from the blinking pixels at a specific frequency. The method is composed of six modules, which includes a band-pass filter and a Kalman filter. All the modules run simultaneously to achieve real-time processing and can run at 500 fps for images with a resolution of 800 \u00d7 600. This technique was verified on an original dataset captured by a high-speed camera under different illumination conditions such as a sunset or night scene. The recall and accuracy justify the generalization of the proposed detection system. In particular, it can detect traffic lights with a different appearance without tuning parameters and without datasets having to be learned.<\/jats:p>","DOI":"10.3390\/s20144035","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T10:59:38Z","timestamp":1595242778000},"page":"4035","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Real-Time Traffic Light Detection with Frequency Patterns Using a High-Speed Camera"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6706-7416","authenticated-orcid":false,"given":"Kento","family":"Yabuuchi","sequence":"first","affiliation":[{"name":"Department of Creative Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Hirano","sequence":"additional","affiliation":[{"name":"Information Technology Center, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8531-1590","authenticated-orcid":false,"given":"Taku","family":"Senoo","sequence":"additional","affiliation":[{"name":"Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norimasa","family":"Kishi","sequence":"additional","affiliation":[{"name":"Information Technology Center, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6096-830X","authenticated-orcid":false,"given":"Masatoshi","family":"Ishikawa","sequence":"additional","affiliation":[{"name":"Information Technology Center, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,20]]},"reference":[{"key":"ref_1","unstructured":"(2020, March 03). Traffic Safety FACTS, Available online: https:\/\/crashstats.nhtsa.dot.gov\/Api\/Public\/ViewPublication\/812115."},{"key":"ref_2","unstructured":"(2020, March 03). The Insurance Institute for Highway Safety (IIHS). Red light running. Available online: https:\/\/www.iihs.org\/topics\/red-light-running."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1109\/TITS.2015.2509509","article-title":"Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives","volume":"17","author":"Jensen","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sooksatra, S., and Kondo, T. (2014, January 14\u201317). Red traffic light detection using fast radial symmetry transform. Proceedings of the 2014 11th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Nakhon Ratchasima, Thailand.","DOI":"10.1109\/ECTICon.2014.6839767"},{"key":"ref_5","unstructured":"Trehard, G., Pollard, E., Bradai, B., and Nashashibi, F. (2014, January 7\u201310). Tracking both pose and status of a traffic light via an Interacting Multiple Model filter. Proceedings of the 17th International Conference on Information Fusion (FUSION), Salamanca, Spain."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wonghabut, P., Kumphong, J., Ung-arunyawee, R., Leelapatra, W., and Satiennam, T. (2018, January 4\u20137). Traffic Light Color Identification for Automatic Traffic Light Violation Detection System. Proceedings of the 2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST), Phuket, Thailand.","DOI":"10.1109\/ICEAST.2018.8434400"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ji, Y., Yang, M., Lu, Z., and Wang, C. (July, January 28). Integrating visual selective attention model with HOG features for traffic light detection and recognition. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea.","DOI":"10.1109\/IVS.2015.7225699"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Du, L., Chen, W., Fu, S., Kong, H., Li, C., and Pei, Z. (2019, January 14\u201317). Real-time Detection of Vehicle and Traffic Light for Intelligent and Connected Vehicles Based on YOLOv3 Network. Proceedings of the 2019 5th International Conference on Transportation Information and Safety (ICTIS), Liverpool, UK.","DOI":"10.1109\/ICTIS.2019.8883761"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, Y., Geng, K., Xue, P., Yin, G., Zhang, N., and Lin, Y. (2019, January 5\u20137). Traffic Lights Detection and Recognition Algorithm Based on Multi-feature Fusion. Proceedings of the 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), Xiamen, China.","DOI":"10.1109\/ICIVC47709.2019.8980828"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Weber, M., Huber, M., and Z\u00f6llner, J.M. (2018, January 4\u20137). HDTLR: A CNN based Hierarchical Detector for Traffic Lights. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569794"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bach, M., Stumper, D., and Dietmayer, K. (2018, January 4\u20137). Deep Convolutional Traffic Light Recognition for Automated Driving. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569522"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fregin, A., M\u00fcller, J., and Dietmayer, K. (2017, January 16\u201319). Feature detectors for traffic light recognition. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317948"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fairfield, N., and Urmson, C. (2011, January 9\u201313). Traffic light mapping and detection. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980164"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Levinson, J., Askeland, J., Dolson, J., and Thrun, S. (2011, January 9\u201313). Traffic light mapping, localization, and state detection for autonomous vehicles. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5979714"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, Z., Watanabe, Y., and Ishikawa, M. (2016, January 1\u20134). Hybrid LED traffic light detection using high-speed camera. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795715"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, Z., Shi, Q., and Huang, X. (July, January 28). Automatic detection of traffic lights using support vector machine. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea.","DOI":"10.1109\/IVS.2015.7225659"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shen, X., Andersen, H., Ang, M.H., and Rus, D. (2017, January 16\u201319). A hybrid approach of candidate region extraction for robust traffic light recognition. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317812"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"M\u00fcller, J., Fregin, A., and Dietmayer, K. (2017, January 16\u201319). Multi-camera system for traffic light detection: About camera setup and mapping of detections. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317946"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Franke, U., Pfeiffer, D., Rabe, C., Knoeppel, C., Enzweiler, M., Stein, F., and Herrtwich, R.G. (2013, January 3\u20136). Making Bertha See. Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops, Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.36"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, J., Cho, H., Hwangbo, M., Choi, J., Canny, J., and Kwon, Y.P. (2018, January 4\u20137). Deep Traffic Light Detection for Self-driving Cars from a Large-scale Dataset. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569575"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Waisakurnia, W., and Widyantoro, D.H. (2016, January 6\u20137). Traffic light candidate elimination based on position. Proceedings of the 2016 10th International Conference on Telecommunication Systems Services and Applications (TSSA), Denpasar, Indonesia.","DOI":"10.1109\/TSSA.2016.7871077"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Possatti, L.C., Guidolini, R., Cardoso, V.B., Berriel, R.F., Paix\u00e3o, T.M., Badue, C., De Souza, A.F., and Oliveira-Santos, T. (2019, January 14\u201319). Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851927"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TMC.2019.2892451","article-title":"Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles","volume":"19","author":"Ouyang","year":"2020","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kulkarni, R., Dhavalikar, S., and Bangar, S. (2018, January 16\u201318). Traffic Light Detection and Recognition for Self Driving Cars Using Deep Learning. Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India.","DOI":"10.1109\/ICCUBEA.2018.8697819"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gupta, A., and Choudhary, A. (2019, January 9\u201312). A Framework for Traffic Light Detection and Recognition using Deep Learning and Grassmann Manifolds. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814062"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aneesh, A.N., Shine, L., Pradeep, R., and Sajith, V. (2019, January 5\u20136). Real-time Traffic Light Detection and Recognition based on Deep RetinaNet for Self Driving Cars. Proceedings of the 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India.","DOI":"10.1109\/ICICICT46008.2019.8993293"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1109\/TITS.2015.2481459","article-title":"Real-Time Traffic Light Detection With Adaptive Background Suppression Filter","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Trans. Intell. Transp. 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