An original thermal dataset designed for training machine learning models for person detection will be presented. 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 video recording, and we compared the image quality at different weather conditions and at different distances in both cases and set parameters that provided the level of detail in the image that can be used to detect the person.
Keywords: Person detection, thermal imaging, surveillance, access control, dataset, biometric
