Loading...
Please wait, while we are loading the content...
A Genetic Approach to Training Support Vector Data Descriptors for Background Modeling in Video Data
| Content Provider | CiteSeerX |
|---|---|
| Author | Tavakkoli, Alireza Ambardekar, Amol Nicolescu, Mircea Louis, Sushil |
| Abstract | Abstract. 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. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Genetic Approach Video Data Background Modeling Training Support Vector Data Descriptor High Level Video Processing Application Important Task Population Sample Accurate Result Lagrange Optimization Problem Foreground Region Quasi-stationary Background Initial Guess Lagrange Optimization Traditional Sequential Minimal Optimization Support Vector Data Description |
| Content Type | Text |
| Resource Type | Article |
