TorrBreg-eccv02 - Space-Time Tracking Lorenzo Torresani and...

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Unformatted text preview: Space-Time Tracking Lorenzo Torresani and Christoph Bregler Computer Science Department, Stanford University, Stanford, CA 94305, USA { ltorresa, bregler } 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 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 con- figurations, allows us to track challenging footage without use of prior models. 1 Introduction Most of the tracking techniques that are able to capture non-rigid motion use a prior model. For instance, some human face-trackers use a pre-trained PCA model or parameterized 3D model, and fit the model to 2D image features. Com- bining these models with advanced sampling techniques (like particle filters or multiple hypothesis approaches) result in algorithms capable of overcoming many local ambiguities. There are many cases where a prior-model is not available. In fact, often the main reason for performing tracking is to estimate data that can be used to build a model. In this case, model-free feature trackers have to be used. Unfortunately many non-rigid domains, such as human motion, contain challenging features that make tracking without a model virtually impossible. Examples of such features are points with degenerate or 1D texture (points along lips and eye contours, cloth and shoe textures). We propose an innovative model-free tracking solution that can overcome such limitations. This can be achieved in redefining the tracking problem. 1. Traditional Tracking : Given M → Estimate α : Standard model-based approaches assume a known (pre-trained) parameter- ized model M ( α ). The model M stays constant over the entire time sequence (for example M might coincide with a set of basis-shapes). The parameters α change from time frame to time frame (for example the interpolation coef- ficients between the basis shapes). Traditional tracking solves by estimating frame by frame the parameters α (1) ..α ( F ) that would fit M ( α ) to the data. 2. Reverse-Order Tracking : Estimate α → Estimate M : Our new technique first estimates the α (1) ..α ( F ) without knowing the model....
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This note was uploaded on 06/13/2011 for the course CAP 6412 taught by Professor Staff during the Spring '08 term at University of Central Florida.

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TorrBreg-eccv02 - Space-Time Tracking Lorenzo Torresani and...

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