A convex penalty method for optical human motion tracking

  • Authors:
  • C. Barrón;I. A. Kakadiaris

  • Affiliations:
  • University of Houston, Houston, TX;University of Houston, Houston, TX

  • Venue:
  • IWVS '03 First ACM SIGMM international workshop on Video surveillance
  • Year:
  • 2003

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Abstract

Human motion tracking from monocular image sequences has been explored widely. However, there is a lack of a framework addressing the variety of sensing conditions. In this paper, we present a simple, efficient, and robust method for recovering plausible 3D motion from a video without knowledge of the camera's parameters. Our method transforms the motion capture problem into a convex problem and employs a hierarchical geometrical solver for the minimization. This algorithm was applied to synthetic and real image sequences with very encouraging results. Specifically, our results indicate that it can handle challenges posed by variation of lighting, partial self-occlusion, and rapid motion.