Carrying Object Detection Using Pose Preserving Dynamic Shape Models

  • Chan-Su Lee
  • Ahmed Elgammal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4069)


In this paper, we introduce a framework for carrying object detection in different people from different views using pose preserving dynamic shape models. We model dynamic shape deformations in different people using kinematics manifold embedding and decomposable generative models by kernel map and multilinear analysis. The generative model supports pose-preserving shape reconstruction in different people, views and body poses. Iterative estimation of shape style and view with pose preserving generative model allows estimation of outlier in addition to accurate body pose. The model is also used for hole filling in the background-subtracted silhouettes using mask generated from the best fitting shape model. Experimental results show accurate estimation of carrying objects with hole filling in discrete and continuous view variations.


Object Detection Outlier Detection Shape Model Shape Deformation Locally Linear Embedding 
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 2006

Authors and Affiliations

  • Chan-Su Lee
    • 1
  • Ahmed Elgammal
    • 1
  1. 1.Department of Computer ScienceRutgers UniversityPiscatawayUSA

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