Camera Motion Estimation Using a Novel Online Vector Field Model in Particle Filters

In this paper, a novel algorithm for parametric camera motion estimation is introduced. More particularly, a novel stochastic vector field model is proposed, which can handle smooth motion patterns derived from long periods of stable camera motion and can also cope with rapid camera motion changes and periods when the camera remains still. The stochastic vector field model is established from a set of noisy measurements, such as motion vectors derived e.g. from block matching techniques, in order to provide an estimation of the subsequent camera motion in the form of a motion vector field. A set of rules for a robust and online update of the camera motion model parameters is also proposed, based on the Expectation Maximization algorithm.