Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras.This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

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Accurately estimating the 3D position of underwater objects is of great interest when doing research on marine animals. An inherent problem of 3D reconstruction of underwater positions is the presence of refraction which invalidates the assumption of a single viewpoint. Three ways of performing 3D reconstruction on underwater objects are compared in this work: an approach relying solely on in-air camera calibration, an approach with the camera calibration performed under water and an approach based on ray tracing with Snell's law. As expected, the in-air camera calibration showed to be the most inaccurate as it does not take refraction into account. The precision of the estimated 3D positions based on the underwater camera calibration and the ray tracing based approach were, on the other hand, almost identical. However, the ray tracing based approach is found to be advantageous as it is far more flexible in terms of the calibration procedure due to the decoupling of the intrinsic and extrinsic camera parameters.

The number of pedestrians walking the streets or gathered in public spaces is a valuable piece of information for shop owners, city governments, event organizers and many others. However, automatic counting that takes place day and night is challenging due to changing lighting conditions and the complexity of scenes with many people occluding one another. To address these challenges, this paper introduces the use of a stereo thermal camera setup for pedestrian counting. We investigate the reconstruction of 3D points in a pedestrian street with two thermal cameras and propose an algorithm for pedestrian counting based on clustering and tracking of the 3D point clouds. The method is tested on two five-minute video sequences captured at a public event with a moderate density of pedestrians and heavy occlusions. The counting performance is compared to the manually annotated ground truth and shows success rates of 95.4% and 99.1% for the two sequences.