Hierarchical Approach for Articulated 3D Pose-Estimation and Tracking

In the recent years we presented a number of methods for a fully automatic pose estimation [5, 7] and tracking [6] of human bodies in 2D [5] and 3D [6]. Initialization and failure recovery in these methods are facilitated by the use of loose-limbed body model [7] in which limbs are connected via learned probabilistic constraints. The pose estimation and tracking can then be formulated as an inference in a loopy graphical model and approximate belief propagation can be used to estimate the pose of the body at each time-step. Each node in the graphical model represents the position and orientation of the limb, and the directed edges between nodes represent statistical dependencies between limbs. There are a number of significant advantages of this paradigm as compared to the more traditional methods for tracking human motion. Most traditional models of the body resort to the kinematic tree-based representations in 2D, 2.5D, or 3D leading to a high-dimensional search space. Searching for a body pose in this high dimensional space is impractical, and so most tracking methods rely on manual initialization or a canonical starting pose. Additionally, they often exploit strong priors characterizing the motions present, to speed up the search. The lack of automatic initialization from an arbitrary pose also makes it hard to recover from transient failures that often occur during tracking. While the full body pose may be hard to recover directly, the location and pose of a sub-set of individual (visible) limbs is often much easier to compute. Many good head detectors exist and limb detectors based on the skin color, shading, and focus have been developed. This observation is what drives forth the loose-limbed body model paradigm, initially introduced in [7]. Here we would like to address the loose-limbed body model within the Bayesian hierarchical framework for 3D pose estimation and tracking from a monocular image sequence recently developed in [4] and [5]. 2. Hierarchical inference framework