A genetic approach to training support vector data descriptors for background modeling in video data

Authors:
Alireza Tavakkoli;Amol Ambardekar;Mircea Nicolescu;Sushil Louis
Affiliations:
Department of Computer Science and Engineering, University of Nevada, Reno;Department of Computer Science and Engineering, University of Nevada, Reno;Department of Computer Science and Engineering, University of Nevada, Reno;Department of Computer Science and Engineering, University of Nevada, Reno
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Year:
2007

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Abstract

Detecting regions of interest in video sequences is one of the most important tasks in many high level video processing applications. In this paper a novel approach based on Support Vector Data Description (SVDD) is presented. The method 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. The Genetic Algorithm (GA) starts with the initial guess and solves the optimization problem iteratively. Moreover, we expect to get accurate results with less cost than the Sequential Minimal Optimization (SMO) technique.