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Reducing drift in parametric motion tracking

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3 Author(s)
Rahimi, A. ; Artificial Intelligence Lab., MIT, Cambridge, MA, USA ; Morency, L.-P. ; Darrell, T.

We develop a class of differential motion trackers that automatically stabilize when in finite domains. Most differential trackers compute motion only relative to one previous frame, accumulating errors indefinitely. We estimate pose changes between a set of past frames, and develop a probabilistic framework for integrating those estimates. We use an approximation to the posterior distribution of pose changes as an uncertainty model for parametric motion in order to help arbitrate the use of multiple base frames. We demonstrate this framework on a simple 2D translational tracker and a 3D, 6-degree of freedom tracker

Published in:

Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on  (Volume:1 )

Date of Conference:

2001