Shape space sampling distributions and their impact on visual tracking

Object motions can be represented as a sequence of shape deformations and translations which can be interpretated as a sequence of points in N-dimensional shape space. These spaces range from simple 2D translations to more inclusive spaces such as the affine. In this case, tracking is the problem of inferring the most likely point in the space for the next frame given a current set of hypotheses. A robust method for achieving this is the particle filter. In this case, likely points within shape space are selected in a two step process. First, image measurements assign likelihoods to proposed points. Likely points are then propagated forward using a dynamical model to derive a set of new points that are perturbed according to some sampling distribution. These distributions play an important role in tracking performance because dynamical models are seldom known and a Gauss Markov model is often assumed for the model dynamics. This paper address the problems inherent in utilizing uninformed sampling distributions for visual tracking. We introduce a principled adaptive sampling approach that takes into account constraints on each component of the shape vector. Further a more appropriate sampling distribution that takes place in a linear subspace representing the predominant motion in the shape space. Results demonstrate improved tracking performance in challenging conditions where targets exhibit changing motion models.