Robust Density Comparison for Visual Tracking
Omar Arif, Patricio Antonio Vela · 2009
This paper presents a technique to robustly compare two distributions represented by samples, without explicitly estimating the density. The method is based on mapping the distributions into a reproducing kernel Hilbert space, where eigenvalue decomposition is performed. Retention of only the top M eigenvectors minimizes the effect of noise on density comparison. A sample application of the technique is visual tracking, where an object is tracked by minimizing the distance between a model distribution and candidate distributions.