Articulated motion reconstruction from feature points

  • Authors:

  • B. Li;Q. Meng;H. Holstein

  • Affiliations:

  • Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, M1 5GD, UK;Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, UK;Department of Computer Science, University of Wales, Aberystwyth, SY23 3DB, Wales, UK

  • Venue:
  • Pattern Recognition
  • Year:
  • 2008

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Abstract

A fundamental task of reconstructing non-rigid articulated motion from sequences of unstructured feature points is to solve the problem of feature correspondence and motion estimation. This problem is challenging in high-dimensional configuration spaces. In this paper, we propose a general model-based dynamic point matching algorithm to reconstruct freeform non-rigid articulated movements from data presented solely by sparse feature points. The algorithm integrates key-frame-based self-initialising hierarchial segmental matching with inter-frame tracking to achieve computation effectiveness and robustness in the presence of data noise. A dynamic scheme of motion verification, dynamic key-frame-shift identification and backward parent-segment correction, incorporating temporal coherency embedded in inter-frames, is employed to enhance the segment-based spatial matching. Such a spatial-temporal approach ultimately reduces the ambiguity of identification inherent in a single frame. Performance evaluation is provided by a series of empirical analyses using synthetic data. Testing on motion capture data for a common articulated motion, namely human motion, gave feature-point identification and matching without the need for manual intervention, in buffered real-time. These results demonstrate the proposed algorithm to be a candidate for feature-based real-time reconstruction tasks involving self-resuming tracking for articulated motion.