A Genetic Approach to Training Support Vector Data Descriptors for Background Modeling in Video Data

Detecting regions of interest in video sequences is one of the most important tasks of most high level video processing applications. In this paper a novel approach based on Support Vector Data Description (SVDD) is presented which detects foreground regions in videos with quasi-stationary backgrounds. The SVDD is a technique used in analytically describing the data from a set of population samples. The training of Support Vector Machines (SVM’s) in general, and SVDD in particular requires a Lagrange optimization which is computationally intensive. We propose to use a genetic approach to solve the Lagrange optimization problem more efficiently. The Genetic Algorithm (GA) starts with an initial guess and solves the optimization problem iteratively. We expect to get accurate results, moreover, with less cost than the traditional Sequential Minimal Optimization (SMO) technique.