Sparse probabilistic regression for activity-independent human pose inference

Discriminative approaches to human pose inference involve mapping visual observations to articulated body configurations. Current probabilistic approaches to learn this mapping have been limited in their ability to handle domains with a large number of activities that require very large training sets. We propose an online probabilistic regression scheme for efficient inference of complex, highdimensional, and multimodal mappings. Our technique is based on a local mixture of Gaussian Processes, where locality is defined based on both appearance and pose, and where the mapping hyperparameters can vary across local neighborhoods to better adapt to specific regions in the pose space. The mixture components are defined online in very small neighborhoods, so learning and inference is extremely efficient. When the mapping is one-to-one, we derive a bound on the approximation error of local regression (vs. global regression) for monotonically decreasing covariance functions. Our method can de...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Raquel Urtasun, Trevor Darrell
• Keywords: Computer Vision, CVPR 2008, Human Pose Inference, Pose Databases, Pose Estimates, Pose Space, Probabilistic Regression Scheme