Optimal estimation applied to visual contour tracking
Ibrahima J. Ndiour, Patricio Antonio Vela · 2010
This paper derives an optimal estimator for the purpose of online visual contour tracking. Starting from Bayesian segmentation as the measurement strategy, we use a bottom-up approach to design the estimator. In particular, it is shown that additive imaging noise leads to multiplicative segmentation uncertainty from which a geometric averaging update model is established. Given known noise statistics, the optimal correction gain and associated filtering equations are derived. The optimal estimator is applied to noise-corrupted imagery and its performance compared against a fixed-gain filtering strategy and other visual tracking techniques.