Thermal image dataset for person detection - UNIRI-TID

We build an original dataset of thermal videos and images that simulate illegal movements around the border and in protected areas and are designed for training machines and deep learning models. The videos are recorded in areas around the forest, at night, in different weather conditions – in the clear weather, in the rain, and in the fog, and with people in different body positions (upright, hunched) and movement speeds (regu- lar walking, running) at different ranges from the camera. In addition to using standard camera lenses, telephoto lenses were also used to test their impact on the quality of thermal images and person detection in different weather conditions and distance from the camera. The obtained dataset comprises 7412 manually labeled images extracted from video frames captured in the long-wave infrared (LWIR) a segment of the electromagnetic (EM) spectrum.本研究构建了一套原创性热成像视频与图像数据集，该数据集模拟了边境及保护区内的非法移动场景，专为机器学习与深度学习模型的训练而设计。视频拍摄于夜间的林区周边区域，涵盖晴朗、降雨、雾天等多种天气条件；场景中包含不同体位（直立、佝偻）、不同移动速度（正常行走、奔跑）的人物，且拍摄距离覆盖了与相机的多种间距。除标准相机镜头外，本数据集还使用了长焦镜头，以测试其在不同天气条件及拍摄距离下，对热成像图像质量与人体检测效果的影响。所构建的数据集包含7412张经人工标注的图像，这些图像提取自拍摄于电磁（EM）频谱长波红外（LWIR）波段的视频帧。

搜集汇总

背景与挑战

背景概述

该数据集是一个专门用于行人检测的热成像图像数据集，包含7412张手动标注的图像，提取自长波红外（LWIR）视频帧。数据采集模拟边境非法移动场景，覆盖多种环境条件（如夜间、不同天气）、人体姿态和运动速度，并测试了不同镜头的影响，适用于训练机器学习和深度学习模型。