Trait opencv::prelude::BackgroundSubtractorMOG2Const

source · 
pub trait BackgroundSubtractorMOG2Const: BackgroundSubtractorConst {

Show 13 methods    fn as_raw_BackgroundSubtractorMOG2(&self) -> *const c_void;

    fn get_history(&self) -> Result<i32> { ... }
    fn get_n_mixtures(&self) -> Result<i32> { ... }
    fn get_background_ratio(&self) -> Result<f64> { ... }
    fn get_var_threshold(&self) -> Result<f64> { ... }
    fn get_var_threshold_gen(&self) -> Result<f64> { ... }
    fn get_var_init(&self) -> Result<f64> { ... }
    fn get_var_min(&self) -> Result<f64> { ... }
    fn get_var_max(&self) -> Result<f64> { ... }
    fn get_complexity_reduction_threshold(&self) -> Result<f64> { ... }
    fn get_detect_shadows(&self) -> Result<bool> { ... }
    fn get_shadow_value(&self) -> Result<i32> { ... }
    fn get_shadow_threshold(&self) -> Result<f64> { ... }

}
Expand description

Gaussian Mixture-based Background/Foreground Segmentation Algorithm.

The class implements the Gaussian mixture model background subtraction described in Zivkovic2004 and Zivkovic2006 .

Required Methods

Provided Methods

Returns the number of last frames that affect the background model

Returns the number of gaussian components in the background model

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 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.

Returns the initial variance of each gaussian component

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 shadow detection flag

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

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 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 Cucchiara, Detecting Moving Shadows…, IEEE PAMI,2003.

Implementors