1 results for Andrew, Smith

  • A Silhouette Based Human Motion Tracking System

    Rosenhahn, Bodo; Kersting, Uwe; Andrew, Smith; Brox, Thomas; Klette, Reinhard; Seidel, Hans-Peter (2005)

    The University of Auckland Library

    You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the original CITR web site; http://citr.auckland.ac.nz/techreports/ under terms that include this permission. All other rights are reserved by the author(s). This paper proposes a system for model based human motion estimation. We start with a human model generation system, which uses a set of input images to automatically generate a free-form surface model of a human upper torso. We subsequently determine joint locations automatically and generate a texture for the surface mesh. Following this, we present morphing and joint transformation techniques to gain more realistic human upper torso models. An advanced model such as this is used in a system for silhouette based human motion estimation. The presented motion estimation system contains silhouette extraction based on level set functions, a correspondence module, which relates image data to model data and a pose estimation module. This system is used for a variety of experiments: Different camera setups (between one to four cameras) are used for the experiments and we estimate the pose configurations of a human upper torso model with 21 degrees of freedom at two frames per second. We also discuss degenerated cases for silhouette based human motion estimation. Next, a comparison of the motion estimation system with a commercial marker based tracking system is performed to gain a quantitative error analysis. The results show the applicability of the system for marker-less human movement analysis. Finally we present experimental results on tracking leg models and show the robustness of our algorithms even for corrupted image data.

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