Multiple pose context trees for estimating human pose in object context

We address the problem of estimating pose in a static image of a human performing an action that may involve interaction with scene objects. In such scenarios, pose can be estimated more accurately using the knowledge of scene objects. Previous approaches do not make use of such contextual information. We propose Pose Context trees to jointly model human pose and object which allows both accurate and efficient inference when the nature of interaction is known. To estimate the pose in an image, we present a Bayesian framework that infers the optimal pose-object pair by maximizing the likelihood over multiple pose context trees for all interactions. We evaluate our approach on a dataset of 65 images, and show that the joint inference of pose and context gives higher pose accuracy.