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Subject: Re: Motion estimation vs. background subtraction
Date: Wed, 12 Mar 2008 15:14:02 +0000 (UTC)
Organization: STFC Rutherford Appleton Laboratory
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"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