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create_bg_esticreate_bg_estiCreateBgEsticreate_bg_estiCreateBgEstiCreateBgEsti (Operator)

Name

create_bg_esticreate_bg_estiCreateBgEsticreate_bg_estiCreateBgEstiCreateBgEsti — Generate and initialize a data set for the background estimation.

Signature

create_bg_esti(InitializeImage : : Syspar1, Syspar2, GainMode, Gain1, Gain2, AdaptMode, MinDiff, StatNum, ConfidenceC, TimeC : BgEstiHandle)

Herror create_bg_esti(const Hobject InitializeImage, double Syspar1, double Syspar2, const char* GainMode, double Gain1, double Gain2, const char* AdaptMode, double MinDiff, const Hlong StatNum, double ConfidenceC, double TimeC, Hlong* BgEstiHandle)

Herror T_create_bg_esti(const Hobject InitializeImage, const Htuple Syspar1, const Htuple Syspar2, const Htuple GainMode, const Htuple Gain1, const Htuple Gain2, const Htuple AdaptMode, const Htuple MinDiff, const Htuple StatNum, const Htuple ConfidenceC, const Htuple TimeC, Htuple* BgEstiHandle)

Herror create_bg_esti(Hobject InitializeImage, const HTuple& Syspar1, const HTuple& Syspar2, const HTuple& GainMode, const HTuple& Gain1, const HTuple& Gain2, const HTuple& AdaptMode, const HTuple& MinDiff, const HTuple& StatNum, const HTuple& ConfidenceC, const HTuple& TimeC, Hlong* BgEstiHandle)

HBgEsti HImage::CreateBgEsti(const HTuple& Syspar1, const HTuple& Syspar2, const HTuple& GainMode, const HTuple& Gain1, const HTuple& Gain2, const HTuple& AdaptMode, const HTuple& MinDiff, const HTuple& StatNum, const HTuple& ConfidenceC, const HTuple& TimeC) const

void HBgEsti::CreateBgEsti(const HImage& InitializeImage, const HTuple& Syspar1, const HTuple& Syspar2, const HTuple& GainMode, const HTuple& Gain1, const HTuple& Gain2, const HTuple& AdaptMode, const HTuple& MinDiff, const HTuple& StatNum, const HTuple& ConfidenceC, const HTuple& TimeC)

void CreateBgEsti(const HObject& InitializeImage, const HTuple& Syspar1, const HTuple& Syspar2, const HTuple& GainMode, const HTuple& Gain1, const HTuple& Gain2, const HTuple& AdaptMode, const HTuple& MinDiff, const HTuple& StatNum, const HTuple& ConfidenceC, const HTuple& TimeC, HTuple* BgEstiHandle)

HBgEsti HImage::CreateBgEsti(double Syspar1, double Syspar2, const HString& GainMode, double Gain1, double Gain2, const HString& AdaptMode, double MinDiff, Hlong StatNum, double ConfidenceC, double TimeC) const

HBgEsti HImage::CreateBgEsti(double Syspar1, double Syspar2, const char* GainMode, double Gain1, double Gain2, const char* AdaptMode, double MinDiff, Hlong StatNum, double ConfidenceC, double TimeC) const

void HBgEsti::HBgEsti(const HImage& InitializeImage, double Syspar1, double Syspar2, const HString& GainMode, double Gain1, double Gain2, const HString& AdaptMode, double MinDiff, Hlong StatNum, double ConfidenceC, double TimeC)

void HBgEsti::HBgEsti(const HImage& InitializeImage, double Syspar1, double Syspar2, const char* GainMode, double Gain1, double Gain2, const char* AdaptMode, double MinDiff, Hlong StatNum, double ConfidenceC, double TimeC)

void HBgEsti::CreateBgEsti(const HImage& InitializeImage, double Syspar1, double Syspar2, const HString& GainMode, double Gain1, double Gain2, const HString& AdaptMode, double MinDiff, Hlong StatNum, double ConfidenceC, double TimeC)

void HBgEsti::CreateBgEsti(const HImage& InitializeImage, double Syspar1, double Syspar2, const char* GainMode, double Gain1, double Gain2, const char* AdaptMode, double MinDiff, Hlong StatNum, double ConfidenceC, double TimeC)

void HOperatorSetX.CreateBgEsti(
[in] IHUntypedObjectX* InitializeImage, [in] VARIANT Syspar1, [in] VARIANT Syspar2, [in] VARIANT GainMode, [in] VARIANT Gain1, [in] VARIANT Gain2, [in] VARIANT AdaptMode, [in] VARIANT MinDiff, [in] VARIANT StatNum, [in] VARIANT ConfidenceC, [in] VARIANT TimeC, [out] VARIANT* BgEstiHandle)

IHBgEstiX* HImageX.CreateBgEsti(
[in] double Syspar1, [in] double Syspar2, [in] BSTR GainMode, [in] double Gain1, [in] double Gain2, [in] BSTR AdaptMode, [in] double MinDiff, [in] Hlong StatNum, [in] double ConfidenceC, [in] double TimeC)

void HBgEstiX.CreateBgEsti(
[in] IHImageX* InitializeImage, [in] double Syspar1, [in] double Syspar2, [in] BSTR GainMode, [in] double Gain1, [in] double Gain2, [in] BSTR AdaptMode, [in] double MinDiff, [in] Hlong StatNum, [in] double ConfidenceC, [in] double TimeC)

static void HOperatorSet.CreateBgEsti(HObject initializeImage, HTuple syspar1, HTuple syspar2, HTuple gainMode, HTuple gain1, HTuple gain2, HTuple adaptMode, HTuple minDiff, HTuple statNum, HTuple confidenceC, HTuple timeC, out HTuple bgEstiHandle)

HBgEsti HImage.CreateBgEsti(double syspar1, double syspar2, string gainMode, double gain1, double gain2, string adaptMode, double minDiff, int statNum, double confidenceC, double timeC)

public HBgEsti(HImage initializeImage, double syspar1, double syspar2, string gainMode, double gain1, double gain2, string adaptMode, double minDiff, int statNum, double confidenceC, double timeC)

void HBgEsti.CreateBgEsti(HImage initializeImage, double syspar1, double syspar2, string gainMode, double gain1, double gain2, string adaptMode, double minDiff, int statNum, double confidenceC, double timeC)

Description

create_bg_esticreate_bg_estiCreateBgEsticreate_bg_estiCreateBgEstiCreateBgEsti creates a new data set for the background estimation and initializes it with the appropriate parameters. The estimated background image is part of this data set. The newly created set automatically becomes the current set.

InitializeImageInitializeImageInitializeImageInitializeImageInitializeImageinitializeImage is used as an initial prediction for the background image. For a good prediction an image of the observed scene without moving objects should be passed in InitializeImageInitializeImageInitializeImageInitializeImageInitializeImageinitializeImage. That way the foreground adaptation rate can be held low. If there is no empty scene image available, a homogeneous gray image can be used instead. In that case the adaptation rate for the foreground image must be raised, because initially most of the image will be detected as foreground. The initialization image must to be of type 'byte'"byte""byte""byte""byte""byte" or 'real'"real""real""real""real""real". Because of processing single-channel images, data sets must be created for every channel. Size and region of InitializeImageInitializeImageInitializeImageInitializeImageInitializeImageinitializeImage determines size and region for all background estimations (run_bg_estirun_bg_estiRunBgEstirun_bg_estiRunBgEstiRunBgEsti) that are performed with this data set.

Syspar1Syspar1Syspar1Syspar1Syspar1syspar1 and Syspar2Syspar2Syspar2Syspar2Syspar2syspar2 are the parameters of the Kalman system matrix. The system matrix describes the system of the gray value changes according to Kalman filter theory. The background estimator implements a different system for each pixel.

GainModeGainModeGainModeGainModeGainModegainMode defines whether a fixed Kalman gain should be used for the estimation or whether the gain should adapt itself depending on the difference between estimation and actual value. If GainModeGainModeGainModeGainModeGainModegainMode is set to 'fixed'"fixed""fixed""fixed""fixed""fixed", then Gain1Gain1Gain1Gain1Gain1gain1 is used as Kalman gain for pixels predicted as foreground and Gain2Gain2Gain2Gain2Gain2gain2 as gain for pixels predicted as background. Gain1Gain1Gain1Gain1Gain1gain1 should be smaller than Gain2Gain2Gain2Gain2Gain2gain2, because adaptation of the foreground should be slower than adaptation of the background. Both Gain1Gain1Gain1Gain1Gain1gain1 and Gain2Gain2Gain2Gain2Gain2gain2 should be smaller than 1.0.

If GainModeGainModeGainModeGainModeGainModegainMode is set to 'frame'"frame""frame""frame""frame""frame", then tables for foreground and background estimation are computed containing Kalman gains for all the 256 possible grayvalue changes. Gain1Gain1Gain1Gain1Gain1gain1 and Gain2Gain2Gain2Gain2Gain2gain2 then denote the number of frames necessary to adapt the difference between estimated value and actual value. So with a fixed time for adaptation (i.e. number of frames) the needed Kalman gain grows with the grayvalue difference. Gain1Gain1Gain1Gain1Gain1gain1 should therefore be larger than Gain2Gain2Gain2Gain2Gain2gain2. Different gains for different grayvalue differences are useful if the background estimator is used for generating an 'empty' scene assuming that there are always moving objects in the observated area. In that case the adaptation time for foreground adaptation (Gain1Gain1Gain1Gain1Gain1gain1) must not be too big. Gain1Gain1Gain1Gain1Gain1gain1 and Gain2Gain2Gain2Gain2Gain2gain2 should be bigger than 1.0.

AdaptModeAdaptModeAdaptModeAdaptModeAdaptModeadaptMode denotes, whether the foreground/background decision threshold applied to the grayvalue difference between estimation and actual value is fixed or whether it adapts itself depending on the grayvalue deviation of the background pixels.

If AdaptModeAdaptModeAdaptModeAdaptModeAdaptModeadaptMode is set to 'off'"off""off""off""off""off", the parameter MinDiffMinDiffMinDiffMinDiffMinDiffminDiff denotes a fixed threshold. The parameters StatNumStatNumStatNumStatNumStatNumstatNum, ConfidenceCConfidenceCConfidenceCConfidenceCConfidenceCconfidenceC and TimeCTimeCTimeCTimeCTimeCtimeC are meaningless in this case.

If AdaptModeAdaptModeAdaptModeAdaptModeAdaptModeadaptMode is set to 'on'"on""on""on""on""on", then MinDiffMinDiffMinDiffMinDiffMinDiffminDiff is interpreted as a base threshold. For each pixel an offset is added to this threshold depending on the statistical evaluation of the pixel value over time. StatNumStatNumStatNumStatNumStatNumstatNum holds the number of data sets (past frames) that are used for computing the grayvalue variance (FIR-Filter). ConfidenceCConfidenceCConfidenceCConfidenceCConfidenceCconfidenceC is used to determine the confidence interval.

The confidence interval determines the values of the background statistics if background pixels are hidden by a foreground object and thus are detected as foreground. According to the student t-distribution the confidence constant is 4.30 (3.25, 2.82, 2.26) for a confidence interval of '99,8%'"99,8%""99,8%""99,8%""99,8%""99,8%" ('99,0%'"99,0%""99,0%""99,0%""99,0%""99,0%", '98,0%'"98,0%""98,0%""98,0%""98,0%""98,0%", '95,0%'"95,0%""95,0%""95,0%""95,0%""95,0%"). TimeCTimeCTimeCTimeCTimeCtimeC holds a time constant for the exp-function that raises the threshold in case of a foreground estimation of the pixel. That means, the threshold is raised in regions where movement is detected in the foreground. That way larger changes in illumination are tolerated if the background becomes visible again. The main reason for increasing this tolerance is the impossibility for a prediction of illumination changes while the background is hidden. Therefore no adaptation of the estimated background image is possible.

Attention

If GainModeGainModeGainModeGainModeGainModegainMode was set to 'frame'"frame""frame""frame""frame""frame", the run-time can be extremely long for large values of Gain1Gain1Gain1Gain1Gain1gain1 or Gain2Gain2Gain2Gain2Gain2gain2, because the values for the gains' table are determined by a simple binary search.

Parallelization

This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.

Parameters

InitializeImageInitializeImageInitializeImageInitializeImageInitializeImageinitializeImage (input_object)  singlechannelimage objectHImageHImageHImageHImageXHobject (byte / real)

initialization image.

Syspar1Syspar1Syspar1Syspar1Syspar1syspar1 (input_control)  real HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

1. system matrix parameter.

Default value: 0.7

Suggested values: 0.65, 0.7, 0.75

Typical range of values: 0.05 ≤ Syspar1 Syspar1 Syspar1 Syspar1 Syspar1 syspar1 ≤ 1.0

Recommended increment: 0.05

Syspar2Syspar2Syspar2Syspar2Syspar2syspar2 (input_control)  real HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

2. system matrix parameter.

Default value: 0.7

Suggested values: 0.65, 0.7, 0.75

Typical range of values: 0.05 ≤ Syspar2 Syspar2 Syspar2 Syspar2 Syspar2 syspar2 ≤ 1.0

Recommended increment: 0.05

GainModeGainModeGainModeGainModeGainModegainMode (input_control)  string HTupleHTupleHTupleVARIANTHtuple (string) (string) (HString) (char*) (BSTR) (char*)

Gain type.

Default value: 'fixed' "fixed" "fixed" "fixed" "fixed" "fixed"

List of values: 'fixed'"fixed""fixed""fixed""fixed""fixed", 'frame'"frame""frame""frame""frame""frame"

Gain1Gain1Gain1Gain1Gain1gain1 (input_control)  real HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Kalman gain / foreground adaptation time.

Default value: 0.002

Suggested values: 10.0, 20.0, 50.0, 0.1, 0.05, 0.01, 0.005, 0.001

Restriction: 0.0 <= Gain1

Gain2Gain2Gain2Gain2Gain2gain2 (input_control)  real HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Kalman gain / background adaptation time.

Default value: 0.02

Suggested values: 2.0, 4.0, 8.0, 0.5, 0.1, 0.05, 0.01

Restriction: 0.0 <= Gain2

AdaptModeAdaptModeAdaptModeAdaptModeAdaptModeadaptMode (input_control)  string HTupleHTupleHTupleVARIANTHtuple (string) (string) (HString) (char*) (BSTR) (char*)

Threshold adaptation.

Default value: 'on' "on" "on" "on" "on" "on"

List of values: 'off'"off""off""off""off""off", 'on'"on""on""on""on""on"

MinDiffMinDiffMinDiffMinDiffMinDiffminDiff (input_control)  real HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Foreground/background threshold.

Default value: 7.0

Suggested values: 3.0, 5.0, 7.0, 9.0, 11.0

Recommended increment: 0.2

StatNumStatNumStatNumStatNumStatNumstatNum (input_control)  integer HTupleHTupleHTupleVARIANTHtuple (integer) (int / long) (Hlong) (Hlong) (Hlong) (Hlong)

Number of statistic data sets.

Default value: 10

Suggested values: 5, 10, 20, 30

Typical range of values: 1 ≤ StatNum StatNum StatNum StatNum StatNum statNum

Recommended increment: 5

ConfidenceCConfidenceCConfidenceCConfidenceCConfidenceCconfidenceC (input_control)  real HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Confidence constant.

Default value: 3.25

Suggested values: 4.30, 3.25, 2.82, 2.62

Recommended increment: 0.01

Restriction: 0.0 < ConfidenceC

TimeCTimeCTimeCTimeCTimeCtimeC (input_control)  real HTupleHTupleHTupleVARIANTHtuple (real) (double) (double) (double) (double) (double)

Constant for decay time.

Default value: 15.0

Suggested values: 10.0, 15.0, 20.0

Recommended increment: 5.0

Restriction: 0.0 < TimeC

BgEstiHandleBgEstiHandleBgEstiHandleBgEstiHandleBgEstiHandlebgEstiHandle (output_control)  bg_estimation HBgEsti, HTupleHTupleHBgEsti, HTupleHBgEstiX, VARIANTHtuple (integer) (IntPtr) (Hlong) (Hlong) (Hlong) (Hlong)

ID of the BgEsti data set.

Example (HDevelop)

* read Init-Image:
read_image (InitImage, 'xing/init')
* initialize 1. BgEsti-Dataset with
* fixed gains and threshold adaption:
create_bg_esti(InitImage,0.7,0.7,'fixed',0.002,0.02, \
               'on',7.0,10,3.25,15.0,BgEstiHandle1)
* initialize 2. BgEsti-Dataset with
* frame orientated gains and fixed threshold
create_bg_esti(InitImage,0.7,0.7,'frame',30.0,4.0, \
               'off',9.0,10,3.25,15.0,BgEstiHandle2)

Example (C)

Hlong Handle1, Handle2;
/* read Init-Image: */
read_image(&InitImage,"xing/init") ;
/* initialize 1. BgEsti-Dataset with
   fixed gains and threshold adaption: */
create_bg_esti(InitImage,0.7,0.7,"fixed",0.002,0.02,
               "on",7.0,10,3.25,15.0:&BgEstiHandle) ;
/* initialize 2. BgEsti-Dataset with
   frame orientated gains and fixed threshold */
create_bg_esti(InitImage,0.7,0.7,"frame",30.0,4.0,
               "off",9.0,10,3.25,15.0:&BgEstiHandle2) ;

Example (HDevelop)

* read Init-Image:
read_image (InitImage, 'xing/init')
* initialize 1. BgEsti-Dataset with
* fixed gains and threshold adaption:
create_bg_esti(InitImage,0.7,0.7,'fixed',0.002,0.02, \
               'on',7.0,10,3.25,15.0,BgEstiHandle1)
* initialize 2. BgEsti-Dataset with
* frame orientated gains and fixed threshold
create_bg_esti(InitImage,0.7,0.7,'frame',30.0,4.0, \
               'off',9.0,10,3.25,15.0,BgEstiHandle2)

Example (HDevelop)

* read Init-Image:
read_image (InitImage, 'xing/init')
* initialize 1. BgEsti-Dataset with
* fixed gains and threshold adaption:
create_bg_esti(InitImage,0.7,0.7,'fixed',0.002,0.02, \
               'on',7.0,10,3.25,15.0,BgEstiHandle1)
* initialize 2. BgEsti-Dataset with
* frame orientated gains and fixed threshold
create_bg_esti(InitImage,0.7,0.7,'frame',30.0,4.0, \
               'off',9.0,10,3.25,15.0,BgEstiHandle2)

Example (HDevelop)

* read Init-Image:
read_image (InitImage, 'xing/init')
* initialize 1. BgEsti-Dataset with
* fixed gains and threshold adaption:
create_bg_esti(InitImage,0.7,0.7,'fixed',0.002,0.02, \
               'on',7.0,10,3.25,15.0,BgEstiHandle1)
* initialize 2. BgEsti-Dataset with
* frame orientated gains and fixed threshold
create_bg_esti(InitImage,0.7,0.7,'frame',30.0,4.0, \
               'off',9.0,10,3.25,15.0,BgEstiHandle2)

Example (HDevelop)

* read Init-Image:
read_image (InitImage, 'xing/init')
* initialize 1. BgEsti-Dataset with
* fixed gains and threshold adaption:
create_bg_esti(InitImage,0.7,0.7,'fixed',0.002,0.02, \
               'on',7.0,10,3.25,15.0,BgEstiHandle1)
* initialize 2. BgEsti-Dataset with
* frame orientated gains and fixed threshold
create_bg_esti(InitImage,0.7,0.7,'frame',30.0,4.0, \
               'off',9.0,10,3.25,15.0,BgEstiHandle2)

Result

create_bg_esticreate_bg_estiCreateBgEsticreate_bg_estiCreateBgEstiCreateBgEsti returns 2 (H_MSG_TRUE) if all parameters are correct.

Possible Successors

run_bg_estirun_bg_estiRunBgEstirun_bg_estiRunBgEstiRunBgEsti

See also

set_bg_esti_paramsset_bg_esti_paramsSetBgEstiParamsset_bg_esti_paramsSetBgEstiParamsSetBgEstiParams, close_bg_esticlose_bg_estiCloseBgEsticlose_bg_estiCloseBgEstiCloseBgEsti

Module

Foundation


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