BackgroundSubtractorMOG2 Class Reference
[Motion Analysis]

Computes a foreground mask.

Parameters:

image	Next video frame.
fgmask	The output foreground mask as an 8-bit binary image.
learningRate	The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.

Clears the algorithm state.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3030 of file core.hpp.

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented in Feature2D, DescriptorMatcher, and StatModel.

Definition at line 3042 of file core.hpp.

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 "background ratio" parameter of the algorithm.

If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's considered background and added to the model as a center of a new component. It corresponds to TB parameter in the paper.

Returns the complexity reduction threshold.

This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05 is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the standard Stauffer&Grimson algorithm.

Returns the algorithm string identifier.

This string is used as top level xml/yml node tag when the object is saved to a file or string.

Returns the shadow detection flag.

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for details.

Returns the number of last frames that affect the background model.

Returns the number of gaussian components in the background model.

Returns the shadow threshold.

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. Tau= 0.5 means that if a pixel is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra, Detecting Moving Shadows...*, IEEE PAMI,2003.

Returns the shadow value.

Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 in the mask always means background, 255 means foreground.

Returns the initial variance of each gaussian component.

Returns the variance threshold for the pixel-model match.

The main threshold on the squared Mahalanobis distance to decide if the sample is well described by the background model or not. Related to Cthr from the paper.

Returns the variance threshold for the pixel-model match used for new mixture component generation.

Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it is considered foreground or added as a new component. 3 sigma => Tg=3\*3=9 is default. A smaller Tg value generates more components. A higher Tg value may result in a small number of components but they can grow too large.

Loads algorithm from the file.

Parameters:

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

Definition at line 3074 of file core.hpp.

Loads algorithm from a String.

Parameters:

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

Definition at line 3094 of file core.hpp.

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     cv::FileStorage fsRead("example.xml", FileStorage::READ);
     Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3055 of file core.hpp.

Reads algorithm parameters from a file storage.

Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3038 of file core.hpp.

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Sets the "background ratio" parameter of the algorithm.

Sets the complexity reduction threshold.

Enables or disables shadow detection.

Sets the number of last frames that affect the background model.

Sets the number of gaussian components in the background model.

The model needs to be reinitalized to reserve memory.

Sets the shadow threshold.

Sets the shadow value.

Sets the initial variance of each gaussian component.

Sets the variance threshold for the pixel-model match.

Sets the variance threshold for the pixel-model match used for new mixture component generation.

Stores algorithm parameters in a file storage.

Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3034 of file core.hpp.