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Robust Spectral 3D-Bodypart Segmentation Along Time

  • Fabio Cuzzolin
  • Diana Mateus
  • Edmond Boyer
  • Radu Horaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4814)

Abstract

In this paper we present a novel tool for body-part segmentation and tracking in the context of multiple camera systems. Our goal is to produce robust motion cues over time sequences, as required by human motion analysis applications. Given time sequences of 3D body shapes, body-parts are consistently identified over time without any supervision or a priori knowledge. The approach first maps shape representations of a moving body to an embedding space using locally linear embedding. While this map is updated at each time step, the shape of the embedded body remains stable. Robust clustering of body parts can then be performed in the embedding space by k-wise clustering, and temporal consistency is achieved by propagation of cluster centroids. The contribution with respect to methods proposed in the literature is a totally unsupervised spectral approach that takes advantage of temporal correlation to consistently segment body-parts over time. Comparisons on real data are run with direct segmentation in 3D by EM clustering and ISOMAP-based clustering: the way different approaches cope with topology transitions is discussed.

Keywords

Cluster Centroid Locally Linear Embedding Topology Transition Linear Embedding Embedding Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fabio Cuzzolin
    • 1
  • Diana Mateus
    • 1
  • Edmond Boyer
    • 1
  • Radu Horaud
    • 1
  1. 1.INRIA Rhone-Alpes, 655 avenue de l’Europe, 38334 MontbonnotFrance

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