Dual Bayesian and Morphology-based Approach for Markerless Human Motion Capture in Natural Interaction Environments

The goal of this work has been to tackle the problem of gestural human-
computer interfaces in its most natural form, i.e. without markers or invasive
devices. In that sense a complete system is proposed in order to classify and
track in real-time a sufficient number of human features that allow novel
forms of gestural man-machine interaction. The algorithm is basically composed
of an intra-image phase and an inter-image phase. The first one takes
advantage of several mathematical morphology tools in order to analyze the
user silhouette and robustly extract head, hands and feet. The second phase
works in an inter-image Bayesian framework in order to achieve the
classification and tracking of the previously extracted features. Due to its
low computational complexity, the system can run at real-time paces on
standard Personal Computers, with an average error rate range between 2% and
7% in realistic situations, depending on the context and segmentation quality.