Patch-Based Pose Inference with a Mixture of Density Estimators

摘要

This paper presents a patch-based approach for pose estimation from single images using a kernelized density voting scheme. We introduce a boosting-like algorithm that models the density using a mixture of weighted 'weak' estimators. The 'weak' density estimators and corresponding weights are learned iteratively from a training set, providing an efficient method for feature selection. Given a query image, voting is performed by reference patches similar in appearance to query image patches. Locality in the voting scheme allows us to handle occlusions and reduces the size of the training set required to cover the space of possible poses and appearance. Finally, the pose is estimated as the dominant mode in the density. Multimodality can be handled by looking at multiple dominant modes. Experiments carried out on face and articulated body pose databases show that our patch-based pose estimation algorithm generalizes well to unseen examples, is robust to occlusions and provides accurate pose estimation.