Context and observation driven latent variable model for human pose estimation

Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human pose from image observations, they are computationally expensive due to search in the high dimensional human pose space. On the other hand, discriminative approaches do not generalize well, but are computationally efficient. We present a hybrid model that combines the strengths of the two in an integrated learning and inference framework. We extend the Gaussian process latent variable model (GPLVM) to include an embedding from observation space (the space of image features) to the latent space. GPLVM is a generative model, but the inclusion of this mapping provides a discriminative component, making the model observation driven. Observation Driven GPLVM (OD-GPLVM) not only provides a faster inference approach, but also more accurate estimates (compared to GPLVM) in cases where dynamics are not sufficient for...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Abhinav Gupta, Trista Chen, Francine Chen, Don Kimber, Larry S. Davis
• Keywords: Computer Vision, CVPR 2008, Discriminative Approaches, Human Pose Estimation, Human Pose Space, Latent Space, Latent Variable Model