Noise estimation and adaptive filtering during visual tracking

This paper proposes a procedure to characterize segmentation-based visual tracking performance with respect to imaging noise. It identifies how imaging noise affects the target segmentation as measured through local shape metrics (Sobolev and Laplace metrics). Such a procedure would be an important calibration step prior to implementing a visual tracking filter for a given need. We utilize the Bhattacharyya coefficient between the target and background intensity distributions to estimate the segmentation error. An empirical study is conducted to establish a correspondence between the Bhattacharyya coefficient and the segmentation error. The correspondence is used to adaptively filter temporally correlated segmentations. Preliminary results show improved performance when compared to fixed gains.