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（thermal videos）与图像数据集，该数据集模拟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训练而设计。该数据集的视频采集于森林周边区域，拍摄时段均为夜间，涵盖多种天气条件：晴朗、降雨及雾天；同时包含姿态各异（直立、佝偻）、移动速度不同（正常行走、奔跑）的行人，且拍摄距离覆盖相机的不同测距范围。除使用标准相机镜头外，本数据集还采用长焦镜头（telephoto lenses），以测试不同天气条件及拍摄距离下，长焦镜头对热成像图像质量与行人检测效果的影响。最终构建的数据集包含7412张人工标注图像，均提取自电磁（EM）频谱中长波红外（long-wave infrared, LWIR）波段下采集的视频帧。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于行人检测的热成像数据集，包含7412张手动标注的图像，提取自长波红外（LWIR）视频帧，模拟边境和保护区周围的非法移动场景。数据在夜间、不同天气条件（晴朗、雨、雾）、人体姿势和运动速度下录制，覆盖不同距离和镜头类型，标注格式支持YOLO、VOC和MS COCO，主要用于训练机器和深度学习模型。