Academic
Publications
A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera

A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera,10.1109/ICCV.2011.6126356,Andreas Baak,Gaurav Bharaj,Hans-Peter

A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera  
BibTex | RIS | RefWorks Download
In recent years, depth cameras have become a widely available sensor type that captures depth images at real- time frame rates. Even though recent approaches have shown that D pose estimation from monocular 2.5D depth images has become feasible, there are still challenging problems due to strong noise in the depth data and self- occlusions in the motions being captured. In this paper, we present an effi cient and robust pose estimation framework for tracking full-body motions from a single depth image stream. Following a data-driven hybrid strategy that com- bines local optimization with global retrieval techniques, we contribute several technical improvements that lead to speed-ups of an order of magnitude compared to previous approaches. In particular, we introduce a variant of Dijk- stra's algorithm to effi ciently extract pose features from the depth data and describe a novel late-fusion scheme based on an effi ciently computable sparse Hausdorff distance to combine local and global pose estimates. Our experiments show that the combination of these techniques facilitates real-time tracking with stable results even for fast and com- plex motions, making it applicable to a wide range of inter- active scenarios.
Conference: International Conference on Computer Vision - ICCV , pp. 1092-1099, 2011
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.