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Background subtraction is an important primitive in computer vision. I'm looking at different methods that have been developed, and I've begun thinking about how to perform background subtraction in the face of random, salt and pepper noise.

In a system such as the Microsoft Kinect, the infrared camera will give off random noise pretty consistently. If you are trying to background subtract from the depth view, how can you avoid an issue with this random noise while reliably subtracting the background?

Jav_Rock
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phantom
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3 Answers3

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as you already said, noise and other unsteady parts of your background might give problems in segmentation, I mean lighting changes or other moving stuff in the background.

But if you're working on some indoor-project this shouldn't be too big of an issue, except of course the noise thing.

Besides substracing the background from an image to segment the objects in it you could also try to subtract two (or in some methods even three) following frames from each other. If the camera is steady this should leave the parts that have changed, so basically the objects that have moved. So this is an easy method for detecting moving objects.

But in most operations you might use you probably will have that noise you described. Easiest way to get rid of it is by using Median Filter or Morpholocigal Operators (Opening) on the segmented binary image. This should effectively remove small parts and leave the nice big blobs of the objects.

Hope that helps...

florianbaethge
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typically you do connected components (cc) in disparity space and then kill any cc that have a small size. The threshold for size and for connectedness (e.g. what is the disparity difference between two adjacent pixel to still consider them connected) are your two parameters to play with (ivlad@lab126.com).

Vlad
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As @evident mentioned, median filter is your ticket. That's the standard operator for getting rid of salt-and-pepper noise while being edge-preserving.

That said, I disagree with his suggestion that this occur on the segmented binary image. Median filtering is very low-level and should be applied on the raw data before any subsequent processing.

peakxu
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