Pose estimation by local procrustes regression

In this paper we propose a new method for appearance-based pose estimation, called Local Procrustes Regression (LPR). In LPR, rather than learning a map between all available training samples and pose space, as is common for appearance-based pose estimation algorithms, the pose of an unknown sample is recovered locally from a small subset of the training samples, by utilizing their inter-point distances. This is accomplished by using Procrustes analysis to align the low-dimensional image subspace generated for the neighborhood of the test sample to its corresponding part of pose space. Experimental results obtained on the Object Pose Estimation Database (OPED) indicate that the proposed method performs on par with state-of-the-art methods like support vector regression.