On Uncertainties, Random Features and Object Tracking
Vijay Badrinarayanan, Patrick Pérez, François Clerc, Lionel Oisel · 2007
Algorithms for probabilistic visual tracking hypothesize a distribution of the target state (location, scale, etc.) at every tracking step with an associated information content or equivalently, an uncertainty. One measure of this uncertainty is the differential entropy. In this paper, we present a unified way to approximate the differential entropy of tracking distributions, which then makes it suitable, among other factors, for a qualitative assessment of both deterministic and sequential Monte Carlo simulation based tracking algorithms. We then illustrate the usefulness of this assessment measure via tracking an object by choosing a set of randomly picked features on it, each individually tracked, removed according to an uncertainty analysis and replaced randomly, without any aid of a feature selection algorithm as in current use.