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Improve openCV Background subtraction

Hi,

I have been looking at openCV Background Subtraction methods ( MOG, MOG2, GMG ,etc).

Final Use Case: Detect (using fixed stereo cameras) a moving Parrot A.R. Drone in a indoor/outdoor environment.

What I need to know: Currently I am trying to explore Background Subtraction but openCV gives very poor results. After seeing the images below, I would like to know your opinion/experience on how these can be improved. I can think of a few methods to improve these results ( eg: MOG + dilation to improve results shown below , usage of HSV/LAB color spaces for potential robustness to lighting conditions,etc)

Thanks!!

Original image frame from a video: $C:\fakepath\original.png$

Comparison of 3 methods below:

MOG $C:\fakepath\mog.png$
MOG2 $C:\fakepath\mog2.png$
GMG $C:\fakepath\gmg.png$

Improve openCV Background subtraction

Hi,

I have been looking at openCV Background Subtraction methods ( MOG, MOG2, GMG ,etc).

Final Use Case: Detect (using fixed stereo cameras) a moving Parrot A.R. Drone in a indoor/outdoor environment.

What I need to know: Currently I am trying to explore Background Subtraction but openCV gives very poor results. After seeing the images below, I would like to know your opinion/experience on how these can be improved. I can think of a few methods to improve these results ( eg: MOG + ~~dilation~~ dilation to improve results shown below , usage of HSV/LAB color spaces for potential robustness to lighting conditions,etc)

Thanks!!

Original image frame from a video: $C:\fakepath\original.png$

Comparison of 3 methods below:

MOG $C:\fakepath\mog.png$
MOG2 $C:\fakepath\mog2.png$
GMG $C:\fakepath\gmg.png$

Improve openCV Background subtraction

Hi,

I have been looking at openCV Background Subtraction methods ( MOG, MOG2, GMG ~~,etc).~~,etc). but it gives very poor results ( see below ).

Final Use Case: Detect (using fixed stereo cameras) a moving Parrot A.R. Drone in a indoor/outdoor environment.

What I need to know: ~~Currently~~

If an object(drone) is moving slow/fast , how would background subtraction algorithm need to be adjusted for that ? My (very little) experience tells me there are a lot of "transparent/black" areas in the output in such cases and I am ~~trying to explore Background Subtraction but openCV gives very poor results.~~ not aware on how this can be avoided.
After seeing the images below, I would like to know your opinion/experience on how these can be improved. I can think of a few methods to improve these results ( eg: MOG + dilation to improve results shown below , usage of HSV/LAB color spaces for potential robustness to lighting conditions,etc)

Thanks!!

Original image frame from a video: $C:\fakepath\original.png$

Comparison of 3 methods below:

MOG $C:\fakepath\mog.png$
MOG2 $C:\fakepath\mog2.png$
GMG $C:\fakepath\gmg.png$

Improve openCV Background subtraction

Hi,

I have been looking at openCV Background Subtraction methods ( MOG, MOG2, GMG ,etc). but it gives very poor results ( see below ).

Final Use Case: Detect (using fixed stereo cameras) a moving Parrot A.R. Drone in a indoor/outdoor environment.

~~What I need to know:~~How you can help me:

If an object(drone) is moving slow/fast , how would background subtraction algorithm need to be adjusted for that ? My (very little) experience tells me there are a lot of "transparent/black" areas in the output in such cases and I am not aware on how this can be avoided.
After seeing the images below, I would like to know your opinion/experience on how these can be improved. I can think of a few methods to improve these results ( eg: MOG + dilation to improve results shown below , usage of HSV/LAB color spaces for potential robustness to lighting conditions,etc)

Thanks!!

Original image frame from a video: $C:\fakepath\original.png$

Comparison of 3 methods below:

MOG $C:\fakepath\mog.png$
MOG2 $C:\fakepath\mog2.png$
GMG $C:\fakepath\gmg.png$

Improve openCV Background subtraction

Hi,

I have been looking at openCV Background Subtraction methods ( MOG, MOG2, GMG ,etc). but it gives very poor results ( see below ).

Final Use Case: Detect (using fixed stereo cameras) a moving Parrot A.R. Drone in a indoor/outdoor environment.

How you can help me:

If an object(drone) is moving slow/fast , how would background subtraction algorithm need to be adjusted for that ? My (very little) experience tells me there are a lot of "transparent/black" areas in the output in such cases and I am not aware on how this can be avoided.
After seeing the images below, I would like to know your opinion/experience on how these can be improved. I can think of a few methods to improve these results ( eg: MOG + dilation to improve results shown below , usage of HSV/LAB color spaces for potential robustness to lighting conditions,etc)

Thanks!!

Original image frame from a video: $C:\fakepath\original.png$

Comparison of 3 methods below:

MOG $C:\fakepath\mog.png$
MOG2 $C:\fakepath\mog2.png$
GMG $C:\fakepath\gmg.png$

Improve openCV Background subtraction

Hi,

I have been looking at openCV Background Subtraction methods ( MOG, MOG2, GMG ,etc). but it gives very poor results ( see below ).

Final Use Case: Detect (using fixed stereo cameras) a moving Parrot A.R. Drone in a indoor/outdoor environment.

How you can help me:

If an object(drone) is moving slow/fast , how would background subtraction algorithm need to be adjusted for that ? My (very little) experience tells me there are a lot of "transparent/black" areas in the output in such cases and I am not aware on how this can be avoided.
After seeing the images below, I would like to know your opinion/experience on how these can be improved. I can think of a few methods to improve these results ( eg: I have tried MOG + dilation to improve results ~~shown below~~ , usage of HSV/LAB color spaces for potential robustness to lighting conditions,etc)

Thanks!!

Original image frame from a video: $C:\fakepath\original.png$

Comparison of 3 methods below:

MOG $C:\fakepath\mog.png$
MOG2 $C:\fakepath\mog2.png$
GMG $C:\fakepath\gmg.png$

Improve openCV Background subtraction

Hi,

I have been looking at openCV Background Subtraction methods ( MOG, MOG2, GMG ,etc). but it gives very poor results ( see below ).

Final Use Case: Detect (using fixed stereo cameras) a moving Parrot A.R. Drone in a indoor/outdoor environment.

How you can help me:

If an object(drone) is moving slow/fast , how would background subtraction algorithm need to be adjusted for that ? My (very little) experience tells me there are a lot of "transparent/black" areas in the output in such cases and I am not aware on how this can be avoided.
After seeing the images below, I would like to know your opinion/experience on how these can be improved. I can think of a few methods to improve these results ( eg: I have tried MOG + dilation to improve results , usage of HSV/LAB color spaces for potential robustness to lighting conditions,etc)

Thanks!!

Original image frame from a ~~video~~video( in which I move drone manually): $C:\fakepath\original.png$

Comparison of 3 methods below:

MOG $C:\fakepath\mog.png$
MOG2 $C:\fakepath\mog2.png$
GMG $C:\fakepath\gmg.png$

Improve openCV Background subtraction

Hi,

Update: I tried using simple absdiff(current_frame, background) + thresholding , and it gives me a better result than all 3 below. I am now curious why openCV has provided these (MOG, MOG2,GMG) Algorithms ? Are they supposed to work better ( than frame difference ) for varying lighting conditions, or with changing static backgound ( background image changing after a few frames have elapsed ) ?

I have been looking at openCV Background Subtraction methods ( MOG, MOG2, GMG ,etc). but it gives very poor results ( see below ).

Final Use Case: Detect (using fixed stereo cameras) a moving Parrot A.R. Drone in a indoor/outdoor environment.

How you can help me:

If an object(drone) is moving slow/fast , how would background subtraction algorithm need to be adjusted for that ? My (very little) experience tells me there are a lot of "transparent/black" areas in the output in such cases and I am not aware on how this can be avoided.
After seeing the images below, I would like to know your opinion/experience on how these can be improved. I can think of a few methods to improve these results ( eg: I have tried MOG + dilation to improve results , usage of HSV/LAB color spaces for potential robustness to lighting conditions,etc)

Thanks!!

Original image frame from a video( in which I move drone manually): $C:\fakepath\original.png$

Comparison of 3 methods below:

MOG $C:\fakepath\mog.png$
MOG2 $C:\fakepath\mog2.png$
GMG $C:\fakepath\gmg.png$