Object tracking using globally coordinated nonlinear manifolds

Che Bin Liu, Ruei Sung Lin, Ming Hsuan Yang, Narendra Ahuja, Stephen Levinson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


We present a dynamic inference algorithm in a globally parameterized nonlinear manifold and demonstrate it on the problem of visual tracking. An appearance manifold is usually nonlinear, embedded in a high dimensional space, and can be approximated by a mixture of locally linear models. Existing methods for nonlinear dimensionality reduction, which map an appearance manifold to a single low dimensional coordinate system, preserve only spatial relationships among manifold points and render low dimensional embeddings rather than mapping functions. In this paper, we parameterize the mixture of linear appearance subspaces of an object in a global coordinate system, and apply it to visual tracking using a Rao-Blackwellized particle filter. Experimental results demonstrate that the proposed approach performs well on object tracking problem in scenes with significant clutter and temporary occlusions which pose difficulties for other methods.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Number of pages4
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other18th International Conference on Pattern Recognition, ICPR 2006
CityHong Kong

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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