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Independent Component Analysis-Based Background Subtraction for Indoor Surveillance

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

abdul-gordon
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Independent Component Analysis-Based Background Subtraction for Indoor Surveillance

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  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

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