Knn background subtraction

As the name suggests, BS calculates the foreground mask performing a subtraction between the current frame and a background model, containing the static part of the scene or, more in general, everything that can be considered as background given the characteristics of the observed scene. In the first step, an initial model of the background is computed, while in the second step that model is updated in order to adapt to possible changes in the scene. In the following you can find the source code.

We will let the user choose to process either a video file or a sequence of images. The output of the program will look as the following for MOG2 method gray areas are detected shadows :.

The output of the program will look as the following for the KNN method gray areas are detected shadows :. OpenCV Tutorials Video analysis video module. How to Use Background Subtraction Methods. Background subtraction BS is a common and widely used technique for generating a foreground mask namely, a binary image containing the pixels belonging to moving objects in the scene by using static cameras.

Downloadable code : Click here Code at glance:. CommandLineParser parser argc, argv, params. You can process both videos and images. VideoCapture capture samples::findFile parser. Mat frame, fgMask. Downloadable code : Click here Code at glance: import org. VideoCapture cv. BackgroundSubtractor backSub.There is some serious inconsistencies between the API and documentation for the Python bindings.

Shows that there is a python implementation for cv2. The MOG2 works good, but the documentation doesn't correspond to the actual parameters in the python version, there is also NO mention of cv2. Sorry if I'm nitpicking, but these are extremely important algortihms and it would be great to have the proper information on them so as to use and implement them properly.

Please lend me your suggestions. I do apologize for the apparent "rudeness" of my question title. I was a bit frustrated when writing due to the fact that this is one of many inconsistencies I've found so far in the documentation. I greatly appreciate the OpenCV framework and I do understand that it is an ongoing open-source project therefore some of these kinds of liberties are not guaranteed. Also, condescendingly telling me to "ask a real question" doesn't make you look any better off attitude wise than me.

knn background subtraction

I asked for clarification about inconsistencies when trying to implement a very specific algorithm and provided a link and descriptions. I don't think it gets more direct than that You didn't ask for clarification about inconsistences, you just pointed out that the docs were outdated and wrong.

Thing that is actually false, because you're mixing 3. Don't expect to be answered politely when you don't ask the same way. And now: 1st read the FAQand 2nd if you're looking for the 3.

Can you verify which version you're seeing this on? Asked: Where is the OpenCV 3. How to find the documentation of Python API? Do all opencv functions support in-place mode for their arguments? How to add documentation to a class? Best site for general documentation. First time here? Check out the FAQ! Hi there! Please sign in help. First of all, be polite while asking a question. And second, ask a real question. NAME cv2. Question Tools Follow.

Related questions Where is the OpenCV 3. Best site for general documentation how to understand which functions available in python bindings?

Update method in Face Recognizer Reference of e.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am trying to substract this building brick. For that I am using the KNN algorithm provided by opencv 3. To initialize the background model I am using 40 frames without the brick.

All in all it works pretty well. Brick with Shadow. After frame 64 I get only black images. Learn more. Asked 4 years, 6 months ago. Active 4 years, 5 months ago. Viewed 1k times. Brick with Shadow The only problem is that the algorithm starts loosing the brick around Frame 58 Image shows frame 62 After frame 64 I get only black images. Does somebody know a solution to this?

Yeah it's a related question, but sadly nobody answered his question. Maybe I am more lucky with my question. Anyway, thank you very much. Active Oldest Votes. A easy but slow solution is to reinitialize the background model every five frames.Background subtraction is a major preprocessing steps in many vision based applications.

For example, consider the cases like visitor counter where a static camera takes the number of visitors entering or leaving the room, or a traffic camera extracting information about the vehicles etc. In all these cases, first you need to extract the person or vehicles alone.

Technically, you need to extract the moving foreground from static background. If you have an image of background alone, like image of the room without visitors, image of the road without vehicles etc, it is an easy job. Just subtract the new image from the background. You get the foreground objects alone. But in most of the cases, you may not have such an image, so we need to extract the background from whatever images we have.

It become more complicated when there is shadow of the vehicles. Since shadow is also moving, simple subtraction will mark that also as foreground. It complicates things. Several algorithms were introduced for this purpose. OpenCV has implemented three such algorithms which is very easy to use.

We will see them one-by-one. KadewTraKuPong and R. Bowden in The weights of the mixture represent the time proportions that those colours stay in the scene. The probable background colours are the ones which stay longer and more static. While coding, we need to create a background object using the function, cv2.

It has some optional parameters like length of history, number of gaussian mixtures, threshold etc. It is all set to some default values. Then inside the video loop, use backgroundsubtractor. It is based on two papers by Z. One important feature of this algorithm is that it selects the appropriate number of gaussian distribution for each pixel.

Remember, in last case, we took a K gaussian distributions throughout the algorithm. It provides better adaptibility to varying scenes due illumination changes etc. As in previous case, we have to create a background subtractor object. Here, you have an option of selecting whether shadow to be detected or not. Shadows will be marked in gray color. This algorithm combines statistical background image estimation and per-pixel Bayesian segmentation.

It was introduced by Andrew B. It uses first few by default frames for background modelling.The concept of background subtraction is really simple. On the video we take the first frame, and we find the absolute difference with another frame.

The result will a mask where in black are the parts that are images in both the images and white the parts that are different. In this way is possible to distinguish the stable background from the objects that are moving.

Keep in mind that this method only works with a stable camera and a stable background. We then take the first frame from the videoconvert it to gray scale and apply Gaussian Blur to remove some noise. And now we get to the core part of the background subtraction where we compute the absolute difference between the first frame and the current frame we are in inside the loop.

The SubtractorMog2 has the advantage of working with a frame history, it works by default with the last frames, but you can change it and we will see later how. Inside the parenthesis we can change the value of the subtractor. History is the number of the last frame that are taken into consideretion by default The threshold value is the value used when computing the difference to extract the background. A lower threshold will find more differences with the advantage of a more noisy image.

Detectshadows is a function of the algorythm that can remove the shadows if enabled. There are no right or wrong values, you need to try different settings to see what best fits your need. Hi, I am a newbie in opencv python. Currently i am having a project related it. I would like to ask how to computes the background model out from the video with using source code of simple subtraction from first frame.

However, i would not like to use it as i prefer to computes the background model out using source code of simple subtraction from first frame. May i know how the way to do that so. If able, can show it with written the code out? I really need to know the way to computes the background model.

Your helping is appreciated.

knn background subtraction

This site uses Akismet to reduce spam. Learn how your comment data is processed. Beginners OpencvTutorials 3. Kelvin June 10, at pm Hi, I am a newbie in opencv python.

SK October 6, at pm how to do this for single image?? This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Necessary Always Enabled.The class implements the K-nearest neighbours background subtraction described in []. Very efficient if number of foreground pixels is low. If true, the algorithm detects shadows and marks them.

The threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to a data sample. K is the number of samples that need to be within dist2Threshold in order to decide that that pixel is matching the kNN background model.

A shadow is detected if pixel is a darker version of the background. The shadow threshold Tau in the paper is a threshold defining how much darker the shadow can be. Shadow value is the value used to mark shadows in the foreground mask. Default value is Value 0 in the mask always means background, means foreground. Public Member Functions List of all members. Returns the shadow detection flag. Returns the threshold on the squared distance between the pixel and the sample.

Returns the number of neighbours, the k in the kNN. Returns the shadow threshold. Returns the shadow value. Sets the number of data samples in the background model. The model needs to be reinitalized to reserve memory. Returns the number of last frames that affect the background model. Returns the number of data samples in the background model. Enables or disables shadow detection.The class implements the K-nearest neighbours background subtraction described in []. Very efficient if number of foreground pixels is low.

Returns true if the Algorithm is empty e.

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If true, the algorithm detects shadows and marks them. The threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to a data sample.

K is the number of samples that need to be within dist2Threshold in order to decide that that pixel is matching the kNN background model. A shadow is detected if pixel is a darker version of the background. The shadow threshold Tau in the paper is a threshold defining how much darker the shadow can be. Shadow value is the value used to mark shadows in the foreground mask. Default value is Value 0 in the mask always means background, means foreground.

This is static template method of Algorithm.

Learning OpenCV 3 Computer Vision with Python - Second Edition by Joe Minichino

It's usage is following in the case of SVM :. Referenced by cv::Feature2D::writeand cv::DescriptorMatcher::write. It differs from the above function only in what argument s it accepts. I'm looking for work. Hire me! Computes a foreground mask.

Parameters image Next video frame. Negative parameter value makes the algorithm to use some automatically chosen learning rate.

knn background subtraction

Clears the algorithm state. Computes a background image. Parameters backgroundImage The output background image. Note Sometimes the background image can be very blurry, as it contain the average background statistics. Returns the algorithm string identifier.

Returns the shadow detection flag.

knn background subtraction

Returns the threshold on the squared distance between the pixel and the sample. Returns the number of neighbours, the k in the kNN. Returns the shadow threshold.

Returns the shadow value. Loads algorithm from the file. Parameters filename Name of the file to read. Loads algorithm from a String.


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