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1. Foreground detection using Gaussian mixture models expand all in page
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vision.ForegroundDetector System object
Package: vision
Description
The ForegroundDetector System object compares a color or grayscale video frame to a background model to
determine whether individual pixels are part of the background or the foreground. It then computes a foreground
mask. By using background subtraction, you can detect foreground objects in an image taken from a stationary
camera.
Note: Starting in R2016b, instead of using the step method to perform the operation
defined by the System object™, you can call the object with arguments, as if it were a
function. For example, y = step(obj,x) and y = obj(x) perform equivalent operations.
Construction
detector = vision.ForegroundDetector returns a foreground detector System object, detector. Given a series
of either grayscale or color video frames, the object computes and returns the foreground mask using Gaussian
mixture models (GMM).
detector = vision.ForegroundDetector(Name,Value) returns a foreground detector System object, detector,
with each specified property name set to the specified value. Name can also be a property name and Value is the
corresponding value. You can specify several name­value pair arguments in any order asName1,Value1,
…,NameN,ValueN.
Code Generation Support
Supports MATLAB Function block: No
Using MATLAB host target: Generates platform­dependent library
Not using MATLAB host target: Generates portable C code
System Objects in MATLAB Code Generation.
Code Generation Support, Usage Notes, and Limitations.
Properties
Adapt learning rate, specified as the comma­separated pair consisting of 'AdaptLearningRate' and a logical
scalar 'true' or 'false'. This property enables the object to adapt the learning rate during the period
specified by theNumTrainingFrames property. When you set this property to true, the object sets
the LearningRate property to 1/(current frame number). When you set this property to false,
the LearningRate property must be set at each time step.
Number of initial video frames for training background model, specified as the comma­separated pair consisting
of 'NumTrainingFrames' and an integer. When you set the AdaptLearningRate to false, this property will not
be available.
Learning rate for parameter updates, specified as the comma­separated pair consisting of 'LearningRate' and
a numeric scalar. 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.
®
AdaptLearningRate — Adapt learning rate
'true' (default) | 'false'
NumTrainingFrames — Number of initial video frames for training background model
150 (default) | integer
LearningRate — Learning rate for parameter updates
0.005 (default) | numeric scalar

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When you set AdaptLearningRate to true, the LearningRate property takes effect only after the training
period specified by NumTrainingFrames is over.
When you set the AdaptLearningRate to false, this property will not be available. This property is tunable.
Threshold to determine background model, specified as the comma­separated pair consisting of
'MinimumBackgroundRatio' and a numeric scalar. Set this property to represent the minimum of the apriori
probabilities for pixels to be considered background values. Multimodal backgrounds can not be handled, if this
value is too small.
Number of Gaussian modes in the mixture model
Number of Gaussian modes in the mixture model, specified as the comma­separated pair consisting of
'NumGaussians' and a positive integer. Typically this value is 3, 4 or 5. Set this value to 3 or greater to be able
to model multiple background modes.
Initial mixture model variance, specified as the comma­separated pair consisting of 'InitialVariance' and as
a numeric scalar or the 'Auto' character vector.
Image Data Type Initial Variance
double/single (30/255)^2
uint8 30^2
This property applies to all color channels for color inputs.
Methods
clone Create foreground detector with same property values
getNumInputs Number of expected inputs to step method
getNumOutputs Number of outputs from step method
isLocked Locked status for input attributes and nontunable properties
release Allow property value and input characteristics changes
reset Reset the GMM model to its initial state
step Detect foreground using Gaussian mixture models
Examples
Create system objects to read file.
videoSource = vision.VideoFileReader('viptraffic.avi',...
'ImageColorSpace','Intensity','VideoOutputDataType','uint8');
Setting frames to 5 because it is a short video. Set initial standard deviation.
MinimumBackgroundRatio — Threshold to determine background model
0.7 (default) | numeric scalar
NumGaussians — Number of Gaussian modes in the mixture model
5 (default) | positive integer
InitialVariance — Initial mixture model variance
'Auto' (default) | numeric scalar
Detect Moving Cars In Video
Open Script

3. detector = vision.ForegroundDetector(...
'NumTrainingFrames', 5, ...
'InitialVariance', 30*30);
Perform blob analysis.
blob = vision.BlobAnalysis(...
'CentroidOutputPort', false, 'AreaOutputPort', false, ...
'BoundingBoxOutputPort', true, ...
'MinimumBlobAreaSource', 'Property', 'MinimumBlobArea', 250);
Insert a border.
shapeInserter = vision.ShapeInserter('BorderColor','White');
Play results. Draw bounding boxes around cars.
videoPlayer = vision.VideoPlayer();
while ~isDone(videoSource)
frame = step(videoSource);
fgMask = step(detector, frame);
bbox = step(blob, fgMask);
out = step(shapeInserter, frame, bbox);
step(videoPlayer, out);
end
Release objects.
release(videoPlayer);
release(videoSource);

4. References
[1] P. Kaewtrakulpong, R. Bowden, An Improved Adaptive Background Mixture Model for Realtime Tracking with
Shadow Detection, In Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01,
VIDEO BASED SURVEILLANCE SYSTEMS: Computer Vision and Distributed Processing (September 2001)
[2] Stauffer, C. and Grimson, W.E.L,Adaptive Background Mixture Models for Real­Time Tracking, Computer Vision
and Pattern Recognition, IEEE Computer Society Conference on, Vol. 2 (06 August 1999), pp. 2246­252 Vol. 2.
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