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.

本团队构建了一个原创的热视频及图像数据集，该数据集模拟了边境及保护区周边的非法活动，并专为训练机器及深度学习模型而设计。视频素材摄于森林周边地区，夜间在不同天气条件下录制——包括晴朗、雨天及雾天，同时，记录了人物在不同身体姿势（直立、弯腰）及不同移动速度（常规步行、奔跑）下的场景，距离摄像头的距离各异。此外，除了使用标准相机镜头外，还采用了长焦镜头以检验其对不同天气条件下热图像质量及人物检测的影响。该数据集包含从长波红外（LWIR）电磁（EM）光谱段捕获的视频帧中提取的7412张手动标注的图像。