In this paper will be presented an original thermal dataset designed for training machine learning models for person detection. The dataset contains 7412 thermal images of humans captured in various scenarios while walking, running, or sneaking. The recordings are captured in the LWIR segment of the electromagnetic (EM) in various weather condition-clear, fog and rain at different distances from the camera, different body positions (upright, hunched) and movement speeds (regular walking, running). In addition to the standard lens of the camera, we used a telephoto lens for recording, and we compared the image quality at different weather conditions and at different distances in both cases to set parameters that provide the level of detail that is enough to detect the person.