Motion Analysis and Object Tracking

Detailed Description

Functions
void	cv::accumulate (InputArray src, InputOutputArray dst, InputArray mask=noArray())
	Adds an image to the accumulator image.
void	cv::accumulateProduct (InputArray src1, InputArray src2, InputOutputArray dst, InputArray mask=noArray())
	Adds the per-element product of two input images to the accumulator image.
void	cv::accumulateSquare (InputArray src, InputOutputArray dst, InputArray mask=noArray())
	Adds the square of a source image to the accumulator image.
void	cv::accumulateWeighted (InputArray src, InputOutputArray dst, double alpha, InputArray mask=noArray())
	Updates a running average.
void	cv::createHanningWindow (OutputArray dst, Size winSize, int type)
	This function computes a Hanning window coefficients in two dimensions.
void	cv::divSpectrums (InputArray a, InputArray b, OutputArray c, int flags, bool conjB=false)
	Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
Point2d	cv::phaseCorrelate (InputArray src1, InputArray src2, InputArray window=noArray(), double *response=0)
	The function is used to detect translational shifts that occur between two images.

Function Documentation

accumulate()

void cv::accumulate	(	InputArray	src,
		InputOutputArray	dst,
		InputArray	mask = noArray() )

Python:
	cv.accumulate(	src, dst[, mask]	) ->	dst

#include <opencv2/imgproc.hpp>

Adds an image to the accumulator image.

The function adds src or some of its elements to dst :

$$\mathtt{\text{dst}}(x,y)\leftarrow \mathtt{\text{dst}}(x,y)+\mathtt{\text{src}}(x,y)\text{if}\mathtt{\text{mask}}(x,y)\ne 0$$

The function supports multi-channel images. Each channel is processed independently.

The function cv::accumulate can be used, for example, to collect statistics of a scene background viewed by a still camera and for the further foreground-background segmentation.

Parameters

src	Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
dst	Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
mask	Optional operation mask.

See also

accumulateSquare, accumulateProduct, accumulateWeighted

accumulateProduct()

void cv::accumulateProduct	(	InputArray	src1,
		InputArray	src2,
		InputOutputArray	dst,
		InputArray	mask = noArray() )

Python:
	cv.accumulateProduct(	src1, src2, dst[, mask]	) ->	dst

#include <opencv2/imgproc.hpp>

Adds the per-element product of two input images to the accumulator image.

The function adds the product of two images or their selected regions to the accumulator dst :

$$\mathtt{\text{dst}}(x,y)\leftarrow \mathtt{\text{dst}}(x,y)+\mathtt{\text{src1}}(x,y)\cdot \mathtt{\text{src2}}(x,y)\text{if}\mathtt{\text{mask}}(x,y)\ne 0$$

The function supports multi-channel images. Each channel is processed independently.

Parameters

src1	First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
src2	Second input image of the same type and the same size as src1 .
dst	Accumulator image with the same number of channels as input images, 32-bit or 64-bit floating-point.
mask	Optional operation mask.

See also

accumulate, accumulateSquare, accumulateWeighted

accumulateSquare()

void cv::accumulateSquare	(	InputArray	src,
		InputOutputArray	dst,
		InputArray	mask = noArray() )

Python:
	cv.accumulateSquare(	src, dst[, mask]	) ->	dst

#include <opencv2/imgproc.hpp>

Adds the square of a source image to the accumulator image.

The function adds the input image src or its selected region, raised to a power of 2, to the accumulator dst :

$$\mathtt{\text{dst}}(x,y)\leftarrow \mathtt{\text{dst}}(x,y)+\mathtt{\text{src}}(x,y{)}^2\text{if}\mathtt{\text{mask}}(x,y)\ne 0$$

The function supports multi-channel images. Each channel is processed independently.

Parameters

src	Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
dst	Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
mask	Optional operation mask.

See also

accumulateSquare, accumulateProduct, accumulateWeighted

accumulateWeighted()

void cv::accumulateWeighted	(	InputArray	src,
		InputOutputArray	dst,
		double	alpha,
		InputArray	mask = noArray() )

Python:
	cv.accumulateWeighted(	src, dst, alpha[, mask]	) ->	dst

#include <opencv2/imgproc.hpp>

Updates a running average.

The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence:

$$\mathtt{\text{dst}}(x,y)\leftarrow (1-\mathtt{\text{alpha}})\cdot \mathtt{\text{dst}}(x,y)+\mathtt{\text{alpha}}\cdot \mathtt{\text{src}}(x,y)\text{if}\mathtt{\text{mask}}(x,y)\ne 0$$

That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). The function supports multi-channel images. Each channel is processed independently.

Parameters

src	Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
dst	Accumulator image with the same number of channels as input image, 32-bit or 64-bit floating-point.
alpha	Weight of the input image.
mask	Optional operation mask.

See also

accumulate, accumulateSquare, accumulateProduct

createHanningWindow()

void cv::createHanningWindow	(	OutputArray	dst,
		Size	winSize,
		int	type )

Python:
	cv.createHanningWindow(	winSize, type[, dst]	) ->	dst

#include <opencv2/imgproc.hpp>

This function computes a Hanning window coefficients in two dimensions.

See (https://en.wikipedia.org/wiki/Hann_function) and (https://en.wikipedia.org/wiki/Window_function) for more information.

An example is shown below:

// create hanning window of size 100x100 and type CV_32F
Mat hann;
createHanningWindow(hann, Size(100, 100), CV_32F);

Parameters

dst	Destination array to place Hann coefficients in
winSize	The window size specifications (both width and height must be > 1)
type	Created array type

divSpectrums()

void cv::divSpectrums	(	InputArray	a,
		InputArray	b,
		OutputArray	c,
		int	flags,
		bool	conjB = false )

Python:
	cv.divSpectrums(	a, b, flags[, c[, conjB]]	) ->	c

#include <opencv2/imgproc.hpp>

Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.

The function cv::divSpectrums performs the per-element division of the first array by the second array. The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.

Parameters

a	first input array.
b	second input array of the same size and type as src1 .
c	output array of the same size and type as src1 .
flags	operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a 0 as value.
conjB	optional flag that conjugates the second input array before the multiplication (true) or not (false).

phaseCorrelate()

Point2d cv::phaseCorrelate	(	InputArray	src1,
		InputArray	src2,
		InputArray	window = noArray(),
		double *	response = 0 )

Python:
	cv.phaseCorrelate(	src1, src2[, window]	) ->	retval, response

#include <opencv2/imgproc.hpp>

The function is used to detect translational shifts that occur between two images.

The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see https://en.wikipedia.org/wiki/Phase_correlation

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window (see https://en.wikipedia.org/wiki/Hann_function) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

  $${\mathbf{G}}_a=\mathcal{F}\{sr{c}_1\},{\mathbf{G}}_b=\mathcal{F}\{sr{c}_2\}$$

  where $\mathcal{F}$ is the forward DFT.
• It then computes the cross-power spectrum of each frequency domain array:

  $$R=\frac{{\mathbf{G}}_a{\mathbf{G}}_b^{\ast }}{|{\mathbf{G}}_a{\mathbf{G}}_b^{\ast }|}$$

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

  $$r={\mathcal{F}}^{-1}\{R\}$$

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

  $$(\mathrm{\Delta }x,\mathrm{\Delta }y)=\mathtt{\text{weightedCentroid}}\{\arg \underset{(x,y)}{max}\{r\}\}$$

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

Parameters

src1	Source floating point array (CV_32FC1 or CV_64FC1)
src2	Source floating point array (CV_32FC1 or CV_64FC1)
window	Floating point array with windowing coefficients to reduce edge effects (optional).
response	Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns

detected phase shift (sub-pixel) between the two arrays.

See also

dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow