Latent variable pictorial structure for human pose estimation on depth images

Prior models of human pose play a key role in state-of-the-art techniques for monocular pose estimation. However, a simple Gaussian model cannot represent well the prior knowledge of the pose diversity on depth images. In this paper, we develop a latent variable-based prior model by introducing a latent variable into the general pictorial structure. Two key characteristics of our model (we call Latent Variable Pictorial Structure) are as follows: (1) it adaptively adopts prior pose models based on the estimated value of the latent variable; and (2) it enables the learning of a more accurate part classifier. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in recognition rate on the public datasets.