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ICCV
2005
IEEE

KALMANSAC: Robust Filtering by Consensus

14 years 6 months ago
KALMANSAC: Robust Filtering by Consensus
We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximumlikelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a samplingbased algorithm that has constant complexity. We derive our algorithm from the equations of the optimal filter, which makes our approximation explicit. Our work is motivated by real-time tracking and the estimation of structure from motion (SFM). We test our algorithm for on-line outlier rejection both for tracking and for SFM. We show that our approach can tolerate a large proportion of outliers, whereas previous causal robust statistical inference methods failed with less than half as many. Our work can be thought of as the extension of random sample consensus algorithms to dynamic data, or as the implementation of pseudo-Bayesian filtering alg...
Andrea Vedaldi, Hailin Jin, Paolo Favaro, Stefano
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICCV
Authors Andrea Vedaldi, Hailin Jin, Paolo Favaro, Stefano Soatto
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