@inproceedings{8e4ed25594db4a0ba75008f7d4e3d474,
title = "A Study on Human Recognition Using General RGB and Thermal Imaging Cameras in Low-Light Environments",
abstract = "This paper addresses the problem that RGB cameras have a relatively low recognition rate for finding people in low-light environments, and aims to use a them1al imaging camera simultaneously to increase the recognition rate for finding people. For this purpose, a bounding box is created based on what is found in the thermal image using the YOL0v8 model, and the boundary line is extracted using the Canny Edge detection algorithm. Then, by calculating the displacement between the border of the high-intensity image and the border of the thermal image, the error of the RGB camera and the them1al imaging camera is shifted upward. Then, adjust the fineness of the boundary line based on the military value of the Canny Edge detection algorithm. As a result, compared to the initial low-light image, the final low-light image with the border selected had a human recognition rate of 22% on average.",
keywords = "Canny Edge Detection, Edges, Low-Light Person Detection, Thermal/RGB Cameras, YOLOv8",
author = "Kim, {So Eun} and Ju, {Hyeon Uk} and Kang, {Tae Won} and Lee, {Han Geyol} and Rhee, {Jong Tae} and Jung, {Jin Woo}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 15th International Conference on Information and Communication Technology Convergence, ICTC 2024 ; Conference date: 16-10-2024 Through 18-10-2024",
year = "2024",
doi = "10.1109/ICTC62082.2024.10826610",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "1966--1969",
booktitle = "ICTC 2024 - 15th International Conference on ICT Convergence",
address = "United States",
}