Analysing high frame-rate camera tracking

High frame-rate offers benefits of robust and accurate camera tracking for rapid motion. However, the benefits are generally understated arguing that it is not possible to operate on high frame-rates due to stringent processing budgets and that even today 1060Hz is treated as a standard real-time frame-rate range. How exactly does the choice of a given frame-rate varies as computational budget is changed? This thesis explores the possibilities of tracking at frame-rates higher than this range and argues that the computational cost per frame in trackers that use prediction is substantially reduced when the frame-rate is increased. Additionally, considering the physics of image formation, high frame-rate implies that the upper bound on the shutter time is reduced leading to less motion blur but more noise. On the other hand, low frame-rate often leads to motion blur but reduced noise in the images. Carefully considering the scene lighting that affects the image noise and the camera motion that affects the motion blur and putting these factors together, how are application-dependent performance requirements of accuracy, robustness and computational cost optimised as frame-rate varies? We study 3D camera tracking from a known rigid model as our test problem and analyse the fundamental image alignment approach to understand the choice of frame-rate that affects tracking. We systematically investigate this via a careful synthesis of photorealistic video using ray-tracing of detailed 3D scene, experimentally obtained photo-realistic reponse and noise models and rapid camera motions and later validate the conclusions with a well-controlled real experiment. The thesis provides quantitative conclusions about frame-rate selection, fundamental connections between frame-rate and image resolution and highlights the crucial role of full consideration of physical image formation process in pushing tracking performance.