Robust 3D human pose estimation via dual dictionaries learning

In this paper, a new dual dictionaries learning (DDL) method is proposed for robust 3D human pose estimation. The performance and applicability of traditional methods are limited by a lack of robustness to corrupted observations caused by occlusions or poor background subtraction. Our DDL approach aims at simultaneously constructing two overcomplete dictionaries, called the visual observation dictionary (VBD) and the body configuration dictionary (BCD), with a shared sparse representation (SSR) regularization with respect to every data sample. Under such regularization, two dictionaries are tied together and the 3D pose estimation problem can be reduced to a simple ℓ¹ optimization problem given a new test visual observation. We also propose a efficient algorithm based on inexact Augmented Lagrange Multiplier (IALM) method to solve the above DDL optimization model. Experimental results on HumanEva database show the superiority of our approach over several current state of the art methods.