Robust Density Comparison for Visual Tracking
نویسندگان
چکیده
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.
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