Motion Analysis#
Detailed Description#
Classes#
Name |
Description |
|---|---|
|
Base class for background/foreground segmentation. : View details |
K-nearest neighbours - based Background/Foreground Segmentation Algorithm. View details |
|
Gaussian Mixture-based Background/Foreground Segmentation Algorithm. View details |
Function Documentation#
createBackgroundSubtractorKNN()#
Ptr< BackgroundSubtractorKNN > cv::createBackgroundSubtractorKNN(
int history = 500,
double dist2Threshold = 400.0,
bool detectShadows = true )
#include <opencv2/video/background_segm.hpp>
Python:
cv.createBackgroundSubtractorKNN([, history[, dist2Threshold[, detectShadows]]]) -> retval
Creates KNN Background Subtractor.
Parameters
history— Length of the history.dist2Threshold— Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.detectShadows— If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.
Here is the call graph for this function:
createBackgroundSubtractorMOG2()#
Ptr< BackgroundSubtractorMOG2 > cv::createBackgroundSubtractorMOG2(
int history = 500,
double varThreshold = 16,
bool detectShadows = true )
#include <opencv2/video/background_segm.hpp>
Python:
cv.createBackgroundSubtractorMOG2([, history[, varThreshold[, detectShadows]]]) -> retval
Creates MOG2 Background Subtractor.
Parameters
history— Length of the history.varThreshold— Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update.detectShadows— If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.