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From: "Abhishek " <highlytoxicboy@gmail.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Motion estimation vs. background subtraction
Date: Mon, 1 Jun 2009 16:24:02 +0000 (UTC)
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"Dave Robinson" <dave.robinson@somewhere.biz> wrote in message <fr8rvq$rfs$1@fred.mathworks.com>...
> "Torvald Helmer" <torvald.helmer@mathworks.com> wrote in 
> message <fr8o5t$m76$1@fred.mathworks.com>...
> > I am conducting a project which consists of object 
> > tracking. There I want to have a good segmentation of the 
> > moving objects from the background. The background may 
> have 
> > noise-factors like light changes, and wind in trees.
> > 
> > What kind of segmentation is best? Should I use just 
> > background subtraction, or is it best with motion 
> > estimation?
> > 
> > Doeas anybody have some thoughts on this subject?
> > 
> > Thanks, Helmer.
> 
> 1) Use a colour camera
> 
> 2) Convert to Normalized RGB to remove the effect of 
> illumination variation.
> 
> 3) Over a protracted period, with no moving target in the 
> scene, calculate the mean background intensity of each 
> individual pixel, together with its associated standard 
> deviation.
> 
> 4) Select a confidence level (e.g. 3xsigma) whereby the 
> difference between the mean image and the current image is 
> deemed to be statistically similar - note that regions that 
> are naturally liable to change (e.g. trees moving) will 
> have a very high sigma, and will consequently be very 
> tolerant of any changes in the scene.
> 
> 5) Generate a binary image of all regions that contain 
> pixels that are outside your confidence range (e.g. 
> Pixel_Difference > 3*PixelSigma;
> 
> 6) Track your moving image using something like blob 
> centroid.
> 
> Regards
> 

> Dave Robinson

Hello Dave,

I tried the same exact method you've put down here, but the normalized RGB image I'm getting is devoid of any worthwhile information of the image. 

Have a look here : 

http://img524.imageshack.us/img524/7079/sdafdfs.png

This is not how it should look, right?