Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking

TitleSequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
Publication TypeJournal Articles
Year of Publication2008
AuthorsHan B, Comaniciu D, Zhu Y, Davis LS
JournalPattern Analysis and Machine Intelligence, IEEE Transactions on
Pagination1186 - 1197
Date Published2008/07//
ISBN Number0162-8828
KeywordsAutomated;Reproducibility of Results;Sensitivity and Specificity;Statistical Distributions;Subtraction Technique;, Computer-Assisted;Models, Gaussian estimation;kernel density estimation;mean-shift mode finding algorithm;online target appearance modeling;probability density function;real-time computer vision application;real-time visual tracking;sequential kernel density approximation;visual f, Statistical;Motion;Pattern Recognition

Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility by fixing or limiting the number of Gaussian components in the mixture or large memory requirement by maintaining a nonparametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm and describe an efficient method to sequentially propagate the density modes over time. Although the proposed density representation is memory efficient, which is typical for mixture densities, it inherits the flexibility of nonparametric methods by allowing the number of components to be variable. The accuracy and compactness of the sequential kernel density approximation technique is illustrated by both simulations and experiments. Sequential kernel density approximation is applied to online target appearance modeling for visual tracking, and its performance is demonstrated on a variety of videos.