MATLAB Object Recognition Algorithm Description: Vision. Foregrounddetector, Vision. Blobanalysis

In the official example, the Motion-based multiple object tracking and the using Kalman Filter for object tracking all use the following two algorithms for object recognition

1, Vision. Foregrounddetector

Principle: The Foregrounddetector System object compares a color or grayscale video frame to a background model to determine Wheth Er individual pixels is part of the background or the foreground. It then computes a foreground mask. By using the background subtraction, you can detect foreground objects on an image taken from a stationary camera.

Each frame and a background frame are compared to determine whether each pixel is a background or a hood (the hood is not the meaning of the background). By this comparison, the moving object in the back frame can be identified. Scope of use: a fixed camera to take the video.

Algorithm: Gaussian mixture models (GMM)., hehe, again Gauss.

See official documentation, examples of how to use them.

Parameters, and the most important part:

adaptlearningrate, Default True, explains:

Enables the object to adapt the learning rate during the period specified by the Numtrainingframes property. When you set the "to true", the object sets the Learningrate property to 1/(current frame number). When you set the "to false", the Learningrate property must is set at each time step.

Very simple, is set to do not learning.

numtrainingframes, default 150, Explanation:

The number of frames used to train the background model, as mentioned above, to identify moving objects by contrast with this background model. Note: These numbers of frames refer to the section at the beginning of the video.

learningrate, default 0.005, explanation:

Learning rate for parameter updates (can't say it, understand it yourself.) ）

Specify the learning rate to adapt model parameters. This property controls how quickly the model adapts to changing conditions. Set this property appropriately to ensure algorithm stability. (Control the update speed of the background model, while this parameter affects the stability of the algorithm)
When you set Adaptlearningrate to True, the Learningrate property is takes effect only after the training period specified by Numtrainingframes is over.

(This parameter is only valid after the initial learning frame, i.e., the last parameter is completed)
When you set the "Adaptlearningrate to False," this property is not being available. This property is tunable.

minimumbackgroundratio, default 0.7, Explanation:

Threshold to determine background model
Set This property to represent the minimum of the apriori probabilities for pixels to be considered background values. Multimodal backgrounds can handled, if this value is too small.

A pixel is considered to be the minimum priori probability of belonging to the background.

Numgaussians, default 5, Explanation:

Number of Gaussian modes in the mixture model (Goss? The great God bless me)
Specify the number of Gaussian modes in the mixture model. Set This property to a positive integer. Typically this value is 3, 4, or 5. Set this value to 3 or greater to is able to model multiple background modes. (Looks like the bigger the better?) And then more conducive to dealing with multiple backgrounds)

initialvariance, Explanation:

Variance when initializing a new Gaussian mode
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MATLAB Object Recognition Algorithm Description: Vision. Foregrounddetector, Vision. Blobanalysis