@Namespace(value="cv::tracking") @Properties(inherit=opencv_tracking.class) public class AugmentedUnscentedKalmanFilterParams extends UnscentedKalmanFilterParams
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter
Constructor and Description |
---|
AugmentedUnscentedKalmanFilterParams() |
AugmentedUnscentedKalmanFilterParams(int dp,
int mp,
int cp,
double processNoiseCovDiag,
double measurementNoiseCovDiag,
UkfSystemModel dynamicalSystem) |
AugmentedUnscentedKalmanFilterParams(int dp,
int mp,
int cp,
double processNoiseCovDiag,
double measurementNoiseCovDiag,
UkfSystemModel dynamicalSystem,
int type) |
AugmentedUnscentedKalmanFilterParams(long size)
Native array allocator.
|
AugmentedUnscentedKalmanFilterParams(Pointer p)
Pointer cast constructor.
|
Modifier and Type | Method and Description |
---|---|
void |
init(int dp,
int mp,
int cp,
double processNoiseCovDiag,
double measurementNoiseCovDiag,
UkfSystemModel dynamicalSystem) |
void |
init(int dp,
int mp,
int cp,
double processNoiseCovDiag,
double measurementNoiseCovDiag,
UkfSystemModel dynamicalSystem,
int type)
The function for initialization of Augmented Unscented Kalman filter
|
AugmentedUnscentedKalmanFilterParams |
position(long position) |
alpha, alpha, beta, beta, CP, CP, dataType, dataType, DP, DP, errorCovInit, errorCovInit, k, k, measurementNoiseCov, measurementNoiseCov, model, model, MP, MP, processNoiseCov, processNoiseCov, stateInit, stateInit
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero
public AugmentedUnscentedKalmanFilterParams(Pointer p)
Pointer.Pointer(Pointer)
.public AugmentedUnscentedKalmanFilterParams(long size)
Pointer.position(long)
.public AugmentedUnscentedKalmanFilterParams()
public AugmentedUnscentedKalmanFilterParams(int dp, int mp, int cp, double processNoiseCovDiag, double measurementNoiseCovDiag, @opencv_core.Ptr UkfSystemModel dynamicalSystem, int type)
dp
- - dimensionality of the state vector,mp
- - dimensionality of the measurement vector,cp
- - dimensionality of the control vector,processNoiseCovDiag
- - value of elements on main diagonal process noise cross-covariance matrix,measurementNoiseCovDiag
- - value of elements on main diagonal measurement noise cross-covariance matrix,dynamicalSystem
- - ptr to object of the class containing functions for computing the next state and the measurement,type
- - type of the created matrices that should be CV_32F or CV_64F.public AugmentedUnscentedKalmanFilterParams(int dp, int mp, int cp, double processNoiseCovDiag, double measurementNoiseCovDiag, @opencv_core.Ptr UkfSystemModel dynamicalSystem)
public AugmentedUnscentedKalmanFilterParams position(long position)
position
in class UnscentedKalmanFilterParams
public void init(int dp, int mp, int cp, double processNoiseCovDiag, double measurementNoiseCovDiag, @opencv_core.Ptr UkfSystemModel dynamicalSystem, int type)
init
in class UnscentedKalmanFilterParams
dp
- - dimensionality of the state vector,mp
- - dimensionality of the measurement vector,cp
- - dimensionality of the control vector,processNoiseCovDiag
- - value of elements on main diagonal process noise cross-covariance matrix,measurementNoiseCovDiag
- - value of elements on main diagonal measurement noise cross-covariance matrix,dynamicalSystem
- - object of the class containing functions for computing the next state and the measurement,type
- - type of the created matrices that should be CV_32F or CV_64F.public void init(int dp, int mp, int cp, double processNoiseCovDiag, double measurementNoiseCovDiag, @opencv_core.Ptr UkfSystemModel dynamicalSystem)
init
in class UnscentedKalmanFilterParams
Copyright © 2020. All rights reserved.