Belief modeling regression for pose estimation

In example-based pose estimation, the configuration or “pose” of an evolving object is sought given visual evidence, having to rely uniquely on a set of examples. In a training stage, a number of features are extracted from the available images and ground truth poses are acquired. In this scenario, a sensible approach consists in learning maps from features to poses, using the training data. In particular, multi-valued mappings linking feature values to a set of training poses can be easily constructed: in this paper, we propose to use these mappings for pose estimation. In the proposed method, a probability measure on any feature space is naturally mapped to a convex set of probabilities on the set of training poses, in a form of a “belief function”. Given a test image, its features translate into a collection of belief functions on the set of training poses, which when combined yield there an entire family of probability distributions. From the latter, both a single, central pose estimate and a set of extremal estimates can be computed, together with a measure of how reliable the estimate is. We call this technique “Belief Modeling Regression”. We demonstrate its effectiveness by comparing it to popular mapping techniques such as Gaussian Process and Relevance Vector Regression under a varied set of experiments.