Entropy/frequency of a natural image I have a C# desktop application.

I am using a wrapper to OpenCV which is EMGU.

I am just performing a basic motion detection operation.

I am using component labelling and 2 frames differencing to determine motion changes.  I am also using an averaging background approach to update the background which i use to 'subtract' from the current and previous frames.

All this works well.

Now consider this image:



Because this bush is close to the camera, movement will be detected by my current methods when the wind blows.

Also, consider this image:



Here we see a cobweb to the right of the image. Again when the wind blows I get motion detection.

My possible solutions:

Look for shapes. In the case of elongated shapes like the cobweb where the height is greater than the width (to a certain scale) then ignore that movement.
But, I found I am missing motion when someone walks on the pavement at the back of the image from across the road.

Erosion.
Again I am missing genuine motion like people and bikes.

Compare Histograms
I have found it is not reliable.  A histogram will only measure the number of different colours (in its own bin).  It is possible for the colours to change due to the saturation and lighting or/and the same numbers of colours are found because another object has passed by.

So, I thought about using atrophy. I did post an earlier question to this here: 

Entropy of an image

but as the person who so kindly explained entropy suggested that I should post as a specific/separate question here...

I want to explore whether the frequency or/and entropy would help me here?  Should I look at spatial or frequency distribution?  Would frequency be best to look into if I want to avoid colour changes?

Would either approach be faster than the other as I want it real-time as possible.

Any eduction for me here would be great.
Entropy would be a good metric in order to separate trees. However I would suggest you to calculate the entropy of gray values within a rectangular window. Consider this image:


After entropy filter, I could obtain this one:



Note that the trees (and also other informative areas) look white and it's possible to segment them out with a simple threshold. Try and see whether it also works for your case.

Due to the possible pre-computations, this could be optimized to run in real-time.