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. We assume here that, in a training stage, a number of feature measurements is extracted from the available images, while an "oracle" provides us with the true object pose at each instant. In this scenario, a sensible approach consists in learning maps from features to poses, using the information provided by the training set. In particular, multi-valued mappings linking feature values to set of training poses can be easily constructed. A probability measure on any feature space is then 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 feature measurements 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. Measuring the conflict among the inferred belief functions provides a way of tackling occlusions and imperfect localization, and flags the need to update the model. We call this technique "Belief Modelling Regression".

Relevant papers:   Fabio Cuzzolin Visions of a generalized probability theory Lambert Academic Publishing, September 2014   Wenjuan Gong and Fabio Cuzzolin A belief-theoretical approach to example-based pose estimation IEEE Transactions on Fuzzy Systems, online 23 March 2017, 10.1109/TFUZZ.2017.2686803 (I.F. 8.746) Fabio Cuzzolin and Ruggero Frezza An evidential reasoning framework for object tracking Proceedings of SPIE - Photonics East 99 - Telemanipulator and Telepresence Technologies VI Vol. 3840, pp. 13-24 Haynes Convention Center, Boston, MA, September 19-22, 1999 Fabio Cuzzolin and Ruggero Frezza Evidential modeling for pose estimation Proceedings of the 4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA'05) Carnegie Mellon University, Pittsburgh, July 2005

Lab Member(s): Wenjuan Gong, Fabio Cuzzolin