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From: "Abhishek " <highlytoxicboy@gmail.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Motion estimation vs. background subtraction
Date: Tue, 2 Jun 2009 14:19:01 +0000 (UTC)
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"Dave Robinson" <dave.robinson@somewhere.biz> wrote in message <h010gj$ch1$1@fred.mathworks.com>...
> "Abhishek " <highlytoxicboy@gmail.com> wrote in message <h00vb2$p8i$1@fred.mathworks.com>...
> > "Dave Robinson" <dave.robinson@somewhere.biz> wrote in message <fr8rvq$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?
> 
> Without looking at your original image, I couldn't really make a judgement, put an full RGB image  on the same site, and I might get a chance to look at it.
> 
> Dave Robinson

Sorry, I should have uploaded the original image first. Anyways, here it is now : 

http://img193.imageshack.us/img193/7710/picture8v.jpg

Here is the Normalized RGB form of the same image - 

http://img193.imageshack.us/img193/5684/11210646.png

I'm using the following relation for conversion : 

R=R/(R+G+B), etc.

Thanks for your help.
Cheers