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vision.OpticalFlow System object

Package: vision

Estimate object velocities

Description

The OpticalFlow System object™ estimates object velocities from one image or video frame to another. It uses either the Horn-Schunck or the Lucas-Kanade method.

    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

opticalFlow = vision.OpticalFlow returns an optical flow System object, opticalFlow. This object estimates the direction and speed of object motion from one image to another or from one video frame to another.

opticalFlow = vision.OpticalFlow(Name,Value) returns an optical flow System object, H, with each specified property set to the specified value. You can specify additional name-value pair arguments in any order as (Name1, Value1,...,NameN,ValueN).

Code Generation Support
Supports Code Generation: No
Code Generation Support, Usage Notes, and Limitations.

To estimate velocity:

  1. Define and set up your text inserter using the constructor.

  2. Call the step method with the input image, I, the optical flow object, opticalFlow, and any optional properties. See the syntax below for using the step method.

VSQ = step(opticalFlow,I) computes the optical flow of input image, I, from one video frame to another, and returns VSQ, specified as a matrix of velocity magnitudes.

V = step(opticalFlow,I) computes the optical flow of input image. I, from one video frame to another, and returns V, specified as a complex matrix of horizontal and vertical components. This applies when you set the OutputValue property to 'Horizontal and vertical components in complex form'.

[...] = step(opticalFlow,I1,I2) computes the optical flow of the input image I1, using I2 as a reference frame. This applies when you set the ReferenceFrameSource property to 'Input port'.

[..., IMV] = step(opticalFlow,I) outputs the delayed input image, IMV. The delay is equal to the latency introduced by the computation of the motion vectors. This property applies when you set the Method property to 'Lucas-Kanade', the TemporalGradientFilter property to 'Derivative of Gaussian', and the MotionVectorImageOutputport property to true.

Properties

Method

Optical flow computation algorithm

Specify the algorithm to compute the optical flow as one of Horn-Schunck | Lucas-Kanade.

Default: Horn-Schunck

ReferenceFrameSource

Source of reference frame for optical flow calculation

Specify computing optical flow as one of Property | Input port. When you set this property to Property, you can use the ReferenceFrameDelay property to determine a previous frame with which to compare. When you set this property to Input port, supply an input image for comparison.

This property applies when you set the Method property to Horn-Schunck. This property also applies when you set the Method property to Lucas-Kanade and the TemporalGradientFilter property to Difference filter [-1 1].

Default: Property

ReferenceFrameDelay

Number of frames between reference frame and current frame

Specify the number of frames between the reference and current frame as a positive scalar integer. This property applies when you set the ReferenceFrameSource property to Current frame and N-th frame back.

Default: 1

Smoothness

Expected smoothness of optical flow

Specify the smoothness factor as a positive scalar number. If the relative motion between the two images or video frames is large, specify a large positive scalar value. If the relative motion is small, specify a small positive scalar value. This property applies when you set the Method property to Horn-Schunck. This property is tunable.

Default: 1

IterationTerminationCondition

Condition to stop iterative solution computation

Specify when the optical flow iterative solution stops. Specify as one of Maximum iteration count | Velocity difference threshold | Either . This property applies when you set the Method property to Horn-Schunck.

Default: Maximum iteration count

MaximumIterationCount

Maximum number of iterations to perform

Specify the maximum number of iterations to perform in the optical flow iterative solution computation as a positive scalar integer. This property applies when you set the Method property to Horn-Schunck and the IterationTerminationCondition property to either Maximum iteration count or Either. This property is tunable.

Default: 10

VelocityDifferenceThreshold

Velocity difference threshold

Specify the velocity difference threshold to stop the optical flow iterative solution computation as a positive scalar number. This property applies when you set the Method property to Horn-Schunck and the IterationTerminationCondition property to either Maximum iteration count or Either. This property is tunable.

Default: eps

OutputValue

Form of velocity output

Specify the velocity output as one of Magnitude-squared | Horizontal and vertical components in complex form.

Default: Magnitude-squared

TemporalGradientFilter

Temporal gradient filter used by Lucas-Kanade algorithm

Specify the temporal gradient filter used by the Lucas-Kanade algorithm as one of Difference filter [-1 1] | Derivative of Gaussian. This property applies when you set the Method property to Lucas-Kanade.

Default: Difference filter [-1 1]

BufferedFramesCount

Number of frames to buffer for temporal smoothing

Specify the number of frames to buffer for temporal smoothing as an odd integer from 3 to 31, both inclusive. This property determines characteristics such as the standard deviation and the number of filter coefficients of the Gaussian filter used to perform temporal filtering. This property applies when you set the Method property to Lucas-Kanade and the TemporalGradientFilter property to Derivative of Gaussian.

Default: 3

ImageSmoothingFilterStandardDeviation

Standard deviation for image smoothing filter

Specify the standard deviation for the Gaussian filter used to smooth the image using spatial filtering. Use a positive scalar number. This property applies when you set the Method property to Lucas-Kanade and the TemporalGradientFilter property to Derivative of Gaussian.

Default: 1.5

GradientSmoothingFilterStandardDeviation

Standard deviation for gradient smoothing filter

Specify the standard deviation for the filter used to smooth the spatiotemporal image gradient components. Use a positive scalar number. This property applies when you set the Method property to Lucas-Kanade and the TemporalGradientFilter property to Derivative of Gaussian.

Default: 1

DiscardIllConditionedEstimates

Discard normal flow estimates when constraint equation is ill-conditioned

When the optical flow constraint equation is ill conditioned, set this property to true so that the motion vector is set to 0. This property applies when you set the Method property to Lucas-Kanade and the TemporalGradientFilter property to Derivative of Gaussian. This property is tunable.

Default: false

MotionVectorImageOutputport

Return image corresponding to motion vectors

Set this property to true to output the image that corresponds to the motion vector being output by the System object. This property applies when you set the Method property to Lucas-Kanade and the TemporalGradientFilter property to Derivative of Gaussian.

Default: false

NoiseReductionThreshold

Threshold for noise reduction

Specify the motion threshold between each image or video frame as a positive scalar number. The higher the number, the less small movements impact the optical flow calculation. This property applies when you set the Method property to Lucas-Kanade. This property is tunable.

Default: 0.0039

 Fixed-Point Properties

Methods

stepEstimate direction and speed of object motion between video frames
Common to All System Objects
clone

Create System object with same property values

getNumInputs

Expected number of inputs to a System object

getNumOutputs

Expected number of outputs of a System object

isLocked

Check locked states of a System object (logical)

release

Allow System object property value changes

Examples

expand all

Set up objects.

videoReader = vision.VideoFileReader('viptraffic.avi','ImageColorSpace','Intensity','VideoOutputDataType','uint8');
converter = vision.ImageDataTypeConverter; 
opticalFlow = vision.OpticalFlow('ReferenceFrameDelay', 1);
opticalFlow.OutputValue = 'Horizontal and vertical components in complex form';
shapeInserter = vision.ShapeInserter('Shape','Lines','BorderColor','Custom', 'CustomBorderColor', 255);
videoPlayer = vision.VideoPlayer('Name','Motion Vector');

Convert the image to single precision, then compute optical flow for the video. Generate coordinate points and draw lines to indicate flow. Display results.

while ~isDone(videoReader)
    frame = step(videoReader);
    im = step(converter, frame);
    of = step(opticalFlow, im);
    lines = videooptflowlines(of, 20);
    if ~isempty(lines)
      out =  step(shapeInserter, im, lines); 
      step(videoPlayer, out);
    end
end

Close the video reader and player

release(videoPlayer);
release(videoReader);

Algorithms

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To compute the optical flow between two images, you must solve the following optical flow constraint equation:

Ixu+Iyv+It=0

.

  • Ix, Iy, and It are the spatiotemporal image brightness derivatives.

  • u is the horizontal optical flow.

  • v is the vertical optical flow.

References

[1] Barron, J.L., D.J. Fleet, S.S. Beauchemin, and T.A. Burkitt. Performance of optical flow techniques. CVPR, 1992.

Introduced in R2012a

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