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Alexander Alekhin
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Alexander Alekhin
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Background Subtraction {#tutorial_py_bg_subtraction} |
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====================== |
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Goal |
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---- |
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In this chapter, |
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- We will familiarize with the background subtraction methods available in OpenCV. |
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Basics |
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------ |
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Background subtraction is a major preprocessing step in many vision-based applications. For |
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example, consider the case of a visitor counter where a static camera takes the number of visitors |
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entering or leaving the room, or a traffic camera extracting information about the vehicles etc. In |
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all these cases, first you need to extract the person or vehicles alone. Technically, you need to |
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extract the moving foreground from static background. |
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If you have an image of background alone, like an image of the room without visitors, image of the road |
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without vehicles etc, it is an easy job. Just subtract the new image from the background. You get |
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the foreground objects alone. But in most of the cases, you may not have such an image, so we need |
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to extract the background from whatever images we have. It becomes more complicated when there are |
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shadows of the vehicles. Since shadows also move, simple subtraction will mark that also as |
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foreground. It complicates things. |
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Several algorithms were introduced for this purpose. OpenCV has implemented three such algorithms |
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which are very easy to use. We will see them one-by-one. |
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### BackgroundSubtractorMOG |
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It is a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. It was introduced in |
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the paper "An improved adaptive background mixture model for real-time tracking with shadow |
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detection" by P. KadewTraKuPong and R. Bowden in 2001. It uses a method to model each background |
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pixel by a mixture of K Gaussian distributions (K = 3 to 5). The weights of the mixture represent |
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the time proportions that those colours stay in the scene. The probable background colours are the |
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ones which stay longer and more static. |
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While coding, we need to create a background object using the function, |
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**cv.createBackgroundSubtractorMOG()**. It has some optional parameters like length of history, |
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number of gaussian mixtures, threshold etc. It is all set to some default values. Then inside the |
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video loop, use backgroundsubtractor.apply() method to get the foreground mask. |
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See a simple example below: |
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@code{.py} |
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import numpy as np |
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import cv2 as cv |
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cap = cv.VideoCapture('vtest.avi') |
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fgbg = cv.bgsegm.createBackgroundSubtractorMOG() |
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while(1): |
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ret, frame = cap.read() |
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fgmask = fgbg.apply(frame) |
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cv.imshow('frame',fgmask) |
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k = cv.waitKey(30) & 0xff |
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if k == 27: |
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break |
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cap.release() |
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cv.destroyAllWindows() |
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@endcode |
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( All the results are shown at the end for comparison). |
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### BackgroundSubtractorMOG2 |
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It is also a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. It is based on two |
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papers by Z.Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction" in 2004 |
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and "Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction" |
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in 2006. One important feature of this algorithm is that it selects the appropriate number of |
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gaussian distribution for each pixel. (Remember, in last case, we took a K gaussian distributions |
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throughout the algorithm). It provides better adaptability to varying scenes due illumination |
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changes etc. |
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As in previous case, we have to create a background subtractor object. Here, you have an option of |
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detecting shadows or not. If detectShadows = True (which is so by default), it |
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detects and marks shadows, but decreases the speed. Shadows will be marked in gray color. |
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@code{.py} |
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import numpy as np |
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import cv2 as cv |
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cap = cv.VideoCapture('vtest.avi') |
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fgbg = cv.createBackgroundSubtractorMOG2() |
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while(1): |
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ret, frame = cap.read() |
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fgmask = fgbg.apply(frame) |
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cv.imshow('frame',fgmask) |
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k = cv.waitKey(30) & 0xff |
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if k == 27: |
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break |
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cap.release() |
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cv.destroyAllWindows() |
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@endcode |
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(Results given at the end) |
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### BackgroundSubtractorGMG |
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This algorithm combines statistical background image estimation and per-pixel Bayesian segmentation. |
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It was introduced by Andrew B. Godbehere, Akihiro Matsukawa, and Ken Goldberg in their paper "Visual |
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Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art |
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Installation" in 2012. As per the paper, the system ran a successful interactive audio art |
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installation called “Are We There Yet?” from March 31 - July 31 2011 at the Contemporary Jewish |
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Museum in San Francisco, California. |
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It uses first few (120 by default) frames for background modelling. It employs probabilistic |
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foreground segmentation algorithm that identifies possible foreground objects using Bayesian |
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inference. The estimates are adaptive; newer observations are more heavily weighted than old |
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observations to accommodate variable illumination. Several morphological filtering operations like |
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closing and opening are done to remove unwanted noise. You will get a black window during first few |
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frames. |
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It would be better to apply morphological opening to the result to remove the noises. |
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@code{.py} |
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import numpy as np |
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import cv2 as cv |
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cap = cv.VideoCapture('vtest.avi') |
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kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE,(3,3)) |
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fgbg = cv.bgsegm.createBackgroundSubtractorGMG() |
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while(1): |
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ret, frame = cap.read() |
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fgmask = fgbg.apply(frame) |
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fgmask = cv.morphologyEx(fgmask, cv.MORPH_OPEN, kernel) |
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cv.imshow('frame',fgmask) |
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k = cv.waitKey(30) & 0xff |
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if k == 27: |
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break |
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cap.release() |
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cv.destroyAllWindows() |
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@endcode |
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Results |
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------- |
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**Original Frame** |
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Below image shows the 200th frame of a video |
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 |
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**Result of BackgroundSubtractorMOG** |
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 |
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**Result of BackgroundSubtractorMOG2** |
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Gray color region shows shadow region. |
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 |
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**Result of BackgroundSubtractorGMG** |
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Noise is removed with morphological opening. |
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 |
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Additional Resources |
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-------------------- |
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Exercises |
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--------- |
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Tutorial content has been moved: @ref tutorial_background_subtraction |
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@ -1,16 +1,4 @@ |
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Video Analysis {#tutorial_py_table_of_contents_video} |
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============== |
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- @ref tutorial_meanshift |
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We have already seen |
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an example of color-based tracking. It is simpler. This time, we see significantly better |
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algorithms like "Meanshift", and its upgraded version, "Camshift" to find and track them. |
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- @ref tutorial_optical_flow |
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Now let's discuss an important concept, "Optical Flow", which is related to videos and has many applications. |
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- @subpage tutorial_py_bg_subtraction |
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In several applications, we need to extract foreground for further operations like object tracking. Background Subtraction is a well-known method in those cases. |
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Content has been moved: @ref tutorial_table_of_content_video |
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