Space-Time Tracking

  • Lorenzo Torresani
  • Christoph Bregler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2350)


We propose a new tracking technique that is able to capture non-rigid motion by exploiting a space-time rank constraint. Most tracking methods use a prior model in order to deal with challenging local features. The model usually has to be trained on carefully hand-labeled example data before the tracking algorithm can be used. Our new model-free tracking technique can overcome such limitations. This can be achieved in redefining the problem. Instead of first training a model and then tracking the model parameters, we are able to derive trajectory constraints first, and then estimate the model. This reduces the search space significantly and allows for a better feature disambiguation that would not be possible with traditional trackers. We demonstrate that sampling in the trajectory space, instead of in the space of shape configurations, allows us to track challenging footage without use of prior models.


Tracking Algorithm Prior Model Rank Constraint Reliable Point Traditional Tracking 
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 2002

Authors and Affiliations

  • Lorenzo Torresani
    • 1
  • Christoph Bregler
    • 1
  1. 1.Computer Science DepartmentStanford UniversityStanfordUSA

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