Density estimation-based information fusion for multiple motion computation

Vision tasks, such as motion analysis, object tracking, robot localization, and 3D modeling, often require the fusion of estimates coming from different sources. Most of the fusion algorithms, however, are not robust with respect to outliers and only consider one source models. Their performance deteriorates when initial assumptions are not valid (e.g., the presence of outliers in the data or data corresponding to multiple motions). The paper presents a statistical solution to the fusion problem based on variable-bandwidth kernel density estimation. The fusion estimate is represented by the mode of a density function that exploits the uncertainty of the estimates to be fused. We show that the fusion estimate is consistent and conservative. Since our construction is founded on density estimation, it handles naturally outliers and multiple source models. We test the density-based fusion for the task of multiple motion computation. Superior experimental results validate our theory.