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National Conference On Electrical Sciences 2014 (NCES-14) ISBN: 978-93-83083-68-8 Department of EEE, Annamacharya Institute of Technology & Sciences, Rajampet Page 2 constraint equation can be given: I(x,y,t)=I(x ∆x,y ∆y,t ∆t) Assuming that the movement is small enough, the image constraint at I(x, y, t) with Taylor series can be derived as I(x ∆x,y ∆y,t ∆t) =I(x,y,z) ∂I/∂x(∆x) ∂I/∂y(∆y) ∂I/∂t(∆t) HOT where H.O.T. means those higher order terms, which are large enough to be ignored. From these equations follows that Δx ∆y ∆t=0 or Which results in The main disadvantage of this Optical flow approach is hard to a apply in real-time due to its high computational cost. B. Temporal Difference Method The Frame difference is arguably the simplest form of background subtraction. The current frame is simply subtracted from the previous frame, and if the difference in pixel values for a given pixel is greater than a threshold (Th), the pixel is considered part of the foreground. A major flaw of this method is that for objects with uniformly distributed intensity values (such as the side of a car), the interior pixels are interpreted as part of the background. Another problem is that objects must be continuously moving. Let Fi-1 be the previous frame and Fi be the consecutive frame, where i= 1 to n. The pseudopodia is given below: For(i=1; i<n; i ) { If (Fi-1 - Fi ) > Th Then accept and process the frame Else Reject the frame } Where ‘Th’ is called as Threshold value. Temporal differencing is very adaptive to dynamic environments, but generally does a poor job of extracting all relevant feature pixels. Due to the disadvantages of this two algorithms we go for background subtraction algorithm. III. PROPOSED BACKGROUND MODELING Background subtraction is a commonly used class of techniques for segmenting out objects of interest in a scene for applications such as surveillance. It compares an observed image with an estimate of the image if it contained no objects of interest. The areas of the image plane where there is a significant difference between the observed and estimated images indicate the location of the objects of interest. The name “background subtraction" comes from the simple technique of subtracting the observed image from the estimated image using basic thresholding process. A. Selective Eigen Background method The main objective of this work is to present an effective Eigen background method that can keep robust in crowded scenes..our method exploits these “selective” mechanisms for background modeling and subtraction, including automatically constructing virtual frames as the training and update samples Eigen backgrounds called selective training, adapting choosing the optical background model for initialization called selective model initialization and Selecting the best Eigen background. For each pixel to reconstruct its background pixel(called pixel-level reconstruction) using thresholding mechanisms our method can significantly increase the purity of the trained Eigen background and obtain an improved quality of reconstructed background image. Moving(i; j) = | Foreground(i; j) - Background(i; j)| When the difference value is greater than the threshold value it is considered as a foreground otherwise it is background object. Fig 1: Flow diagram for proposed algorithm In the first step we consider the video and convert it into frames, from that frames we can select any of two images for that two images we create a header file .And then we
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