1 / 11

Independent Component Analysis-Based Background Subtraction for Indoor Surveillance

Independent Component Analysis-Based Background Subtraction for Indoor Surveillance. INTRODUCTION.

abdul-gordon
Télécharger la présentation

Independent Component Analysis-Based Background Subtraction for Indoor Surveillance

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Independent Component Analysis-Based BackgroundSubtraction for Indoor Surveillance

  2. INTRODUCTION • propose a simple and fast background subtraction scheme without background model updating, and yet it is tolerable to changes in room lighting for indoor surveillance using Independent Component Analysis (ICA).

  3. ICA-BASED BACKGROUND SUBTRACTION A. Basic ICA Model • The observed mixture signals X can be expressed as X = AS where A is an unknown mixing matrix, and S represents the latent source signals. • The ICA model describes how the observed mixture signals X are generated by a process that uses the mixing matrix A to mix the latent source signals S.

  4. ICA-BASED BACKGROUND SUBTRACTION A. Basic ICA Model • The source signals are assumed to be mutually statistically independent. Based on the assumption, the ICA solution is obtained in an unsupervised learning process that finds a de-mixing matrix W. The matrix W is used to transform the observed mixture signals X to yield the independent signals, i.e., WX = Y. The independent signals Y are used as the estimates of the latent source signals S. The components of Y, called independent components, are required to be as mutually independent as possible.

  5. ICA-BASED BACKGROUND SUBTRACTION B. Proposed ICA Model • In order to separate foreground objects from the background in a scene image, we need at least two sample images to construct the mixture signals in the ICA model. • Let the sample images be of size m X n. Each sample image is organized as a row vector of K dimensions, where K = m ∙ n. Denote by Xb = [xb1 , xb2 , ∙ ∙ ∙ , xbK]the reference background image containing no foreground objects, and Xf = [xf1 , xf2 , ∙ ∙ ∙ , xfK]the foreground image containing an arbitrary foreground object in the stationary background.

  6. ICA-BASED BACKGROUND SUBTRACTION B. Proposed ICA Model • In the training stage of the proposed background subtraction scheme, the ICA model is given by Y = W∙XT

  7. EXPERIMENTAL RESULTS • Single Reference Background

  8. EXPERIMENTAL RESULTS • Single Reference Background

  9. EXPERIMENTAL RESULTS • Multiple Reference Background

  10. EXPERIMENTAL RESULTS • Multiple Reference Background

  11. EXPERIMENTAL RESULTS • Effect of Varying Foreground Objects

More Related