Abstract

We propose a new tracking technique that is able to cap- ture 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 tra jectory 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 tra jectory space, instead of in the space of shape con- figurations, allows us to track challenging footage without use of prior models.