cv::optflow::RLOFOpticalFlowParameter Class Reference

This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm. More...

#include <opencv2/optflow/rlofflow.hpp>

Public Member Functions
	RLOFOpticalFlowParameter ()
int	getCrossSegmentationThreshold () const
float	getGlobalMotionRansacThreshold () const
int	getLargeWinSize () const
int	getMaxIteration () const
int	getMaxLevel () const
float	getMinEigenValue () const
float	getNormSigma0 () const
float	getNormSigma1 () const
int	getSmallWinSize () const
SolverType	getSolverType () const
SupportRegionType	getSupportRegionType () const
bool	getUseGlobalMotionPrior () const
bool	getUseIlluminationModel () const
bool	getUseInitialFlow () const
void	setCrossSegmentationThreshold (int val)
void	setGlobalMotionRansacThreshold (float val)
void	setLargeWinSize (int val)
void	setMaxIteration (int val)
void	setMaxLevel (int val)
void	setMinEigenValue (float val)
void	setNormSigma0 (float val)
void	setNormSigma1 (float val)
void	setSmallWinSize (int val)
void	setSolverType (SolverType val)
void	setSupportRegionType (SupportRegionType val)
void	setUseGlobalMotionPrior (bool val)
void	setUseIlluminationModel (bool val)
void	setUseInitialFlow (bool val)
void	setUseMEstimator (bool val)
	Enable M-estimator or disable and use least-square estimator. More...

Static Public Member Functions
static Ptr< RLOFOpticalFlowParameter >	create ()
	Creates instance of optflow::RLOFOpticalFlowParameter. More...

Public Attributes
int	crossSegmentationThreshold
float	globalMotionRansacThreshold
int	largeWinSize
int	maxIteration
int	maxLevel
float	minEigenValue
float	normSigma0
float	normSigma1
int	smallWinSize
SolverType	solverType
SupportRegionType	supportRegionType
bool	useGlobalMotionPrior
bool	useIlluminationModel
bool	useInitialFlow

Detailed Description

This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm.

The RLOF is a fast local optical flow approach described in [209] [210] [211] and [212] similar to the pyramidal iterative Lucas-Kanade method as proposed by [25]. More details and experiments can be found in the following thesis [213]. The implementation is derived from optflow::calcOpticalFlowPyrLK(). This RLOF implementation can be seen as an improved pyramidal iterative Lucas-Kanade and includes a set of improving modules. The main improvements in respect to the pyramidal iterative Lucas-Kanade are:

• A more robust redecending M-estimator framework (see [209]) to improve the accuracy at motion boundaries and appearing and disappearing pixels.
• an adaptive support region strategies to improve the accuracy at motion boundaries to reduce the corona effect, i.e oversmoothing of the PLK at motion/object boundaries. The cross-based segementation strategy (SR_CROSS) proposed in [211] uses a simple segmenation approach to obtain the optimal shape of the support region.
• To deal with illumination changes (outdoor sequences and shadow) the intensity constancy assumption based optical flow equation has been adopt with the Gennert and Negahdaripour illumination model (see [212]). This model can be switched on/off with the useIlluminationModel variable.
• By using a global motion prior initialization (see [212]) of the iterative refinement the accuracy could be significantly improved for large displacements. This initialization can be switched on and of with useGlobalMotionPrior variable.

The RLOF can be computed with the SparseOpticalFlow class or function interface to track a set of features or with the DenseOpticalFlow class or function interface to compute dense optical flow.

See also

optflow::DenseRLOFOpticalFlow, optflow::calcOpticalFlowDenseRLOF(), optflow::SparseRLOFOpticalFlow, optflow::calcOpticalFlowSparseRLOF()

Constructor & Destructor Documentation

RLOFOpticalFlowParameter()

cv::optflow::RLOFOpticalFlowParameter::RLOFOpticalFlowParameter ( )

inline

Member Function Documentation

create()

static Ptr<RLOFOpticalFlowParameter> cv::optflow::RLOFOpticalFlowParameter::create ( )

static

Python:
	retval	=	cv.optflow.RLOFOpticalFlowParameter_create(		)

Creates instance of optflow::RLOFOpticalFlowParameter.

getCrossSegmentationThreshold()

int cv::optflow::RLOFOpticalFlowParameter::getCrossSegmentationThreshold

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getCrossSegmentationThreshold(		)

getGlobalMotionRansacThreshold()

float cv::optflow::RLOFOpticalFlowParameter::getGlobalMotionRansacThreshold

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getGlobalMotionRansacThreshold(		)

getLargeWinSize()

int cv::optflow::RLOFOpticalFlowParameter::getLargeWinSize

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getLargeWinSize(		)

getMaxIteration()

int cv::optflow::RLOFOpticalFlowParameter::getMaxIteration

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getMaxIteration(		)

getMaxLevel()

int cv::optflow::RLOFOpticalFlowParameter::getMaxLevel

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getMaxLevel(		)

getMinEigenValue()

float cv::optflow::RLOFOpticalFlowParameter::getMinEigenValue

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getMinEigenValue(		)

getNormSigma0()

float cv::optflow::RLOFOpticalFlowParameter::getNormSigma0

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getNormSigma0(		)

getNormSigma1()

float cv::optflow::RLOFOpticalFlowParameter::getNormSigma1

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getNormSigma1(		)

getSmallWinSize()

int cv::optflow::RLOFOpticalFlowParameter::getSmallWinSize

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getSmallWinSize(		)

getSolverType()

SolverType cv::optflow::RLOFOpticalFlowParameter::getSolverType

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getSolverType(		)

getSupportRegionType()

SupportRegionType cv::optflow::RLOFOpticalFlowParameter::getSupportRegionType

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getSupportRegionType(		)

getUseGlobalMotionPrior()

bool cv::optflow::RLOFOpticalFlowParameter::getUseGlobalMotionPrior

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getUseGlobalMotionPrior(		)

getUseIlluminationModel()

bool cv::optflow::RLOFOpticalFlowParameter::getUseIlluminationModel

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getUseIlluminationModel(		)

getUseInitialFlow()

bool cv::optflow::RLOFOpticalFlowParameter::getUseInitialFlow

(

)

const

Python:
	retval	=	cv.optflow_RLOFOpticalFlowParameter.getUseInitialFlow(		)

setCrossSegmentationThreshold()

void cv::optflow::RLOFOpticalFlowParameter::setCrossSegmentationThreshold

(

int

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setCrossSegmentationThreshold(	val	)

setGlobalMotionRansacThreshold()

void cv::optflow::RLOFOpticalFlowParameter::setGlobalMotionRansacThreshold

(

float

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setGlobalMotionRansacThreshold(	val	)

setLargeWinSize()

void cv::optflow::RLOFOpticalFlowParameter::setLargeWinSize

(

int

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setLargeWinSize(	val	)

setMaxIteration()

void cv::optflow::RLOFOpticalFlowParameter::setMaxIteration

(

int

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setMaxIteration(	val	)

setMaxLevel()

void cv::optflow::RLOFOpticalFlowParameter::setMaxLevel

(

int

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setMaxLevel(	val	)

setMinEigenValue()

void cv::optflow::RLOFOpticalFlowParameter::setMinEigenValue

(

float

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setMinEigenValue(	val	)

setNormSigma0()

void cv::optflow::RLOFOpticalFlowParameter::setNormSigma0

(

float

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setNormSigma0(	val	)

setNormSigma1()

void cv::optflow::RLOFOpticalFlowParameter::setNormSigma1

(

float

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setNormSigma1(	val	)

setSmallWinSize()

void cv::optflow::RLOFOpticalFlowParameter::setSmallWinSize

(

int

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setSmallWinSize(	val	)

setSolverType()

void cv::optflow::RLOFOpticalFlowParameter::setSolverType

(

SolverType

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setSolverType(	val	)

setSupportRegionType()

void cv::optflow::RLOFOpticalFlowParameter::setSupportRegionType

(

SupportRegionType

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setSupportRegionType(	val	)

setUseGlobalMotionPrior()

void cv::optflow::RLOFOpticalFlowParameter::setUseGlobalMotionPrior

(

bool

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setUseGlobalMotionPrior(	val	)

setUseIlluminationModel()

void cv::optflow::RLOFOpticalFlowParameter::setUseIlluminationModel

(

bool

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setUseIlluminationModel(	val	)

setUseInitialFlow()

void cv::optflow::RLOFOpticalFlowParameter::setUseInitialFlow

(

bool

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setUseInitialFlow(	val	)

setUseMEstimator()

void cv::optflow::RLOFOpticalFlowParameter::setUseMEstimator

(

bool

val

)

Python:
	None	=	cv.optflow_RLOFOpticalFlowParameter.setUseMEstimator(	val	)

Enable M-estimator or disable and use least-square estimator.

Enables M-estimator by setting sigma parameters to (3.2, 7.0). Disabling M-estimator can reduce runtime, while enabling can improve the accuracy.

Parameters

val	If true M-estimator is used. If false least-square estimator is used.

See also

setNormSigma0, setNormSigma1

Member Data Documentation

crossSegmentationThreshold

int cv::optflow::RLOFOpticalFlowParameter::crossSegmentationThreshold

Color similarity threshold used by cross-based segmentation following [211] . (Only used if supportRegionType is SR_CROSS). With the cross-bassed segmentation motion boundaries can be computed more accurately.

globalMotionRansacThreshold

float cv::optflow::RLOFOpticalFlowParameter::globalMotionRansacThreshold

To apply the global motion prior motion vectors will be computed on a regulary sampled which are the basis for Homography estimation using RANSAC. The reprojection threshold is based on n-th percentil (given by this value [0 ... 100]) of the motion vectors magnitude. See [212] for more details.

largeWinSize

int cv::optflow::RLOFOpticalFlowParameter::largeWinSize

Maximal window size of the support region. If supportRegionType is SR_FIXED this gives the exact support region size. The speed of the RLOF is related to the applied win sizes. The smaller the window size the lower is the runtime, but the more sensitive to noise is the method.

maxIteration

int cv::optflow::RLOFOpticalFlowParameter::maxIteration

Number of maximal iterations used for the iterative refinement. Lower values can reduce the runtime but also the accuracy.

maxLevel

int cv::optflow::RLOFOpticalFlowParameter::maxLevel

Maximal number of pyramid level used. The large this value is the more likely it is to obtain accurate solutions for long-range motions. The runtime is linear related to this parameter.

minEigenValue

float cv::optflow::RLOFOpticalFlowParameter::minEigenValue

Threshold for the minimal eigenvalue of the gradient matrix defines when to abort the iterative refinement.

normSigma0

float cv::optflow::RLOFOpticalFlowParameter::normSigma0

&sigma parameter of the shrinked Hampel norm introduced in [209]. If &sigma = std::numeric_limist<float>::max() the least-square estimator will be used instead of the M-estimator. Althoug M-estimator is more robust against outlier in the support region the least-square can be fast in computation.

normSigma1

float cv::optflow::RLOFOpticalFlowParameter::normSigma1

&sigma parameter of the shrinked Hampel norm introduced in [209]. If &sigma = std::numeric_limist<float>::max() the least-square estimator will be used instead of the M-estimator. Althoug M-estimator is more robust against outlier in the support region the least-square can be fast in computation.

smallWinSize

int cv::optflow::RLOFOpticalFlowParameter::smallWinSize

Minimal window size of the support region. This parameter is only used if supportRegionType is SR_CROSS.

solverType

SolverType cv::optflow::RLOFOpticalFlowParameter::solverType

Variable specifies the iterative refinement strategy. Please consider citing [210] when using ST_BILINEAR.

supportRegionType

SupportRegionType cv::optflow::RLOFOpticalFlowParameter::supportRegionType

Variable specifies the support region shape extraction or shrinking strategy.

useGlobalMotionPrior

bool cv::optflow::RLOFOpticalFlowParameter::useGlobalMotionPrior

Use global motion prior initialisation has been introduced in [212] . It allows to be more accurate for long-range motion. The computational complexity is slightly increased by enabling the global motion prior initialisation.

useIlluminationModel

bool cv::optflow::RLOFOpticalFlowParameter::useIlluminationModel

Use the Gennert and Negahdaripour illumination model instead of the intensity brigthness constraint. (proposed in [212] ) This model is defined as follow:

\[ I(\mathbf{x},t) + m \cdot I(\mathbf{x},t) + c = I(\mathbf{x},t+1) \]

and contains with m and c a multiplicative and additive term which makes the estimate more robust against illumination changes. The computational complexity is increased by enabling the illumination model.

useInitialFlow

bool cv::optflow::RLOFOpticalFlowParameter::useInitialFlow

Use next point list as initial values. A good intialization can imporve the algortihm accuracy and reduce the runtime by a faster convergence of the iteration refinement.

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The documentation for this class was generated from the following file:

• opencv2/optflow/rlofflow.hpp