Online full body human motion tracking based on dense volumetric 3D reconstructions from multi camera setups

Authors:
Tobias Feldmann;Ioannis Mihailidis;Sebastian Schulz;Dietrich Paulus;Annika Wörner
Affiliations:
Institute for Anthropomatics, Department of Informatics, Karlsruhe Institute of Technology;Institute for Computational Visualistics, Computer Science Faculty, University Koblenz-Landau;Institute for Anthropomatics, Department of Informatics, Karlsruhe Institute of Technology;Institute for Computational Visualistics, Computer Science Faculty, University Koblenz-Landau;Institute for Anthropomatics, Department of Informatics, Karlsruhe Institute of Technology
Venue:
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Year:
2010

Citing 3
Cited 1

A survey of computer vision-based human motion capture

Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Single-Frame 3D Human Pose Recovery from Multiple Views

Proceedings of the 31st DAGM Symposium on Pattern Recognition
A system for marker-less human motion estimation

PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition

Multi-sensor 3D volumetric reconstruction using CUDA

3D Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an approach for video based human motion capture using a static multi camera setup. The image data of calibrated video cameras is used to generate dense volumetric reconstructions of a person within the capture volume. The 3d reconstructions are then used to fit a 3d cone model into the data utilizing the Iterative Closest Point (ICP) algorithm. We can show that it is beneficial to use multi camera data instead of a single time of flight camera to gain more robust results in the overall tracking approach.