Controlled human pose estimation from depth image streams

This paper presents a model-based, Cartesian control theoretic approach for estimating human pose from features detected using depth images obtained from a time of flight imaging device. The features represent positions of anatomical landmarks, detected and tracked over time based on a probabilistic inferencing algorithm. The detected features are subsequently used as input to a constrained, closed loop tracking control algorithm which not only estimates the pose of the articulated human model, but also provides feedback to the feature detector in order to resolve ambiguities or to provide estimates of undetected features. Based on a simple kinematic model, constraints such as joint limit avoidance, and self penetration avoidance are enforced within the tracking control framework. We demonstrate the effectiveness of the algorithm with experimental results of upper body pose reconstruction from a small set of features. On average, the entire pipeline runs at approximately 10 frames per second on a standard 3 GHz PC using a 17 degree of freedom upper body human model.