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 10-
60Hz 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.