opencv BackgroundSubtractorMOG2重要方法

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最新推荐文章于 2025-02-06 08:00:00 发布

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分类专栏： opencv VA 点滴积累 c/c++ 文章标签： opencv 背景建模

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BackgroundSubtractorMOG2 重要方法

1. BackgroundSubtractorMOG2 的构造函数

C++: BackgroundSubtractorMOG2::BackgroundSubtractorMOG2()

采用默认值进行构造BackgroundSubtractorMOG2的对象。

C++: BackgroundSubtractorMOG2:: BackgroundSubtractorMOG2(int history, float varThreshold, bool bShadowDetection=true)

采用指定值进行构造BackgroundSubtractorMOG2的对象。

参数说明：

Parameters:

Parameters:	history – Length of the history. varThreshold – Threshold on the squared Mahalanobis distance to decide whether it is well described by the background model (see Cthr??). This parameter does not affect the background update. A typical value could be 4 sigma, that is, `varThreshold=44=16;` (see Tb??). bShadowDetection* – Parameter defining whether shadow detection should be enabled (`true` or `false`).

history – Length of the history.
varThreshold – Threshold on the squared Mahalanobis distance to decide whether it is well described by the background model (see Cthr??). This parameter does not affect the background update. A typical value could be 4 sigma, that is, varThreshold=4*4=16; (see Tb??).
bShadowDetection – Parameter defining whether shadow detection should be enabled (true or false).

2. BackgroundSubtractorMOG2 的operate

C++: void BackgroundSubtractorMOG2::operator()(InputArray image, OutputArray fgmask, double learningRate=-1)

参数说明： 参数image为待处理的图像；fgmask得到的前景图像（二值化都的）；learningRate(0~1)配置背景更新方法，0表示不更新，1表示根据最后一帧更新，负数表示自动更新，(0~1)数字越大，背景更新越快。

Parameters:

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.

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.

以上理解如果做错，欢迎交流。