Robust object pose estimation via statistical manifold modeling

We propose a novel statistical manifold modeling approach that is capable of classifying poses of object categories from video sequences by simultaneously minimizing the intra-class variability and maximizing inter-pose distance. Following the intuition that an object part based representation and a suitable part selection process may help achieve our purpose, we formulate the part selection problem from a statistical manifold modeling perspective and treat part selection as adjusting the manifold of the object (parameterized by pose) by means of the manifold “alignment” and “expansion” operations. We show that manifold alignment and expansion are equivalent to minimizing the intra-class distance given a pose while increasing the inter-pose distance given an object instance respectively. We formulate and solve this (otherwise intractable) part selection problem as a combinatorial optimization problem using graph analysis techniques. Quantitative and qualitative experimental analysis validates our theoretical claims.