We describe two new approaches to human pose estimation. Both can quickly and
accurately predict the 3D positions of body joints from a single depth image without using …

We present a fast, automatic method for accurately capturing full-body motion data using a
single depth camera. At the core of our system lies a realtime registration process that …

Random forests have been successfully applied to various high level computer vision tasks
such as human pose estimation and object segmentation. These models are extremely …

Tracking human pose in real-time is a difficult problem with many interesting applications.
Existing solutions suffer from a variety of problems, especially when confronted with unusual …

A novel real-time algorithm for head and hand tracking is proposed in this paper. This
approach is based on data from a range camera, which is exploited to resolve ambiguities …

In this paper we share our experiences with the design and use of motion recognition for
assistive systems in production environments (ASiPE) and introduce a concept for …

Depth sensors such as the Microsoft Kinect™ depth sensor provide three dimensional point
clouds of an observed scene. In this paper, we employ Random Hypersurface Models …

Industrial robotics is a challenging domain for cognitive systems, especially, when human
intelligence meets solid machinery with many degrees of freedom like most of today's …

Markerless pose inference of arbitrary subjects is a primary problem for a variety of
applications, including robot vision and teaching by demonstration. Unsupervised kinematic …

While Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point
clouds, they do not take into account constraints arising from sensor viewpoints. More recent …