General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues

Markerless motion capture algorithms require a 3D body with properly personalized skeleton dimension and/or body shape and appearance to successfully track a person. Unfortunately, many tracking methods consider model personalization a different problem and use manual or semi-automatic model initialization, which greatly reduces applicability. In this paper, we propose a fully automatic algorithm that jointly creates a rigged actor model commonly used for animation - skeleton, volumetric shape, appearance, and optionally a body surface - and estimates the actor's motion from multi-view video input only. The approach is rigorously designed to work on footage of general outdoor scenes recorded with very few cameras and without background subtraction. Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy. For reconstruction, 3D body shape is approximated as Gaussian density field. For pose and shape estimation, we minimize a new edge-based alignment energy inspired by volume raycasting in an absorbing medium. We further propose a new statistical human body model that represents the body surface, volumetric Gaussian density, as well as variability in skeleton shape. Given any multi-view sequence, our method jointly optimizes the pose and shape parameters of this model fully automatically in a spatiotemporal way

Using mutual information for multi-anchor tracking of human beings

Tracking of human beings represents a hot research topic in the field of video analysis. It is attracting an increasing attention among researchers thanks to its possible application in many challenging tasks. Among these, action recognition, human/human and human/computer interaction require bodypart tracking. Most of the existing techniques in literature are model-based approaches, so despite their effectiveness, they are often unfit for the specific requirements of a body-part tracker. In this case it is very hard if not impossible to define a formal model of the target. This paper proposes a multi-anchor tracking system, which works on 8 bits color images and exploits the mutual information to track human body parts (head, hands, ...) without performing any foreground/background segmentation. The proposed method has been designed as a component of a more general system aimed at human interaction analysis. It has been tested on a wide set of color video sequences and the very promising results show its high potential

Using Mutual Information for Multi-Anchor Tracking of Human Beings

Tracking of human beings represents a hot research topic in the field of video analysis. It is attracting an increasing attention among researchers thanks to its possible application in many challenging tasks. Among these, action recognition, human/human and human/computer interaction require body-part tracking. Most of the existing techniques in literature are model-based approaches, so despite their effectiveness, they are often unfit for the specific requirements of a body-part tracker. In this case it is very hard if not impossible to define a formal model of the target. This paper proposes a multi-anchor tracking system, which works on 8 bits color images and exploits the mutual information to track human body parts (head, hands, …) without performing any foreground/background segmentation. The proposed method has been designed as a component of a more general system aimed at human interaction analysis. It has been tested on a wide set of color video sequences and the very promising results show its high potential

Nonparametric Density Estimation with Adaptive Anisotropic Kernels for Human Motion Tracking

Abstract. In this paper, we suggest to model priors on human motion by means of nonparametric kernel densities. Kernel densities avoid assumptions on the shape of the underlying distribution and let the data speak for themselves. In general, kernel density estimators suffer from the problem known as the curse of dimensionality, i.e., the amount of data required to cover the whole input space grows exponentially with the dimension of this space. In many applications, such as human motion tracking, though, this problem turns out to be less severe, since the relevant data concentrate in a much smaller subspace than the original high-dimensional space. As we demonstrate in this paper, the concentration of human motion data on lower-dimensional manifolds, approves kernel density estimation as a transparent tool that is able to model priors on arbitrary mixtures of human motions. Further, we propose to support the ability of kernel estimators to capture distributions on lowdimensional manifolds by replacing the standard isotropic kernel by an adaptive, anisotropic one.