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

In recent years, depth cameras have become a widely available sensor type that captures depth images at realtime frame rates. Even though recent approaches have shown that 3D 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 selfocclusions in the motions being captured. In this paper, we present an efficient and robust pose estimation framework for tracking full-body motions from a single depth image stream. Following a data-driven hybrid strategy that combines 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 Dijkstra’s algorithm to efficiently extract pose features from the depth data and describe a novel late-fusion scheme based on an efficiently computable sparse Hausdorff distance to combine local and global pose...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Andreas Baak, Meinard Muller, Gaurav Bharaj, Hans-Peter Seidel, Christian Theobalt
• Keywords: Body Motions, Computer Vision, Global Retrieval, ICCV 2011, Interactive Scenarios