| Operators |
create_bg_esti — Generate and initialize a data set for the background estimation.
create_bg_esti(InitializeImage : : Syspar1, Syspar2, GainMode, Gain1, Gain2, AdaptMode, MinDiff, StatNum, ConfidenceC, TimeC : BgEstiHandle)
create_bg_esti 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.
InitializeImage 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 InitializeImage. 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' or 'real'. Because of processing single-channel images, data sets must be created for every channel. Size and region of InitializeImage determines size and region for all background estimations (run_bg_esti) that are performed with this data set.
Syspar1 and Syspar2 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.
GainMode 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 GainMode is set to 'fixed', then Gain1 is used as Kalman gain for pixels predicted as foreground and Gain2 as gain for pixels predicted as background. Gain1 should be smaller than Gain2, because adaptation of the foreground should be slower than adaptation of the background. Both Gain1 and Gain2 should be smaller than 1.0.
If GainMode is set to 'frame', then tables for foreground and background estimation are computed containing Kalman gains for all the 256 possible grayvalue changes. Gain1 and Gain2 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. Gain1 should therefore be larger than Gain2. 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 (Gain1) must not be too big. Gain1 and Gain2 should be bigger than 1.0.
AdaptMode 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 AdaptMode is set to 'off', the parameter MinDiff denotes a fixed threshold. The parameters StatNum, ConfidenceC and TimeC are meaningless in this case.
If AdaptMode is set to 'on', then MinDiff 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. StatNum holds the number of data sets (past frames) that are used for computing the grayvalue variance (FIR-Filter). ConfidenceC 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%' ('99,0%', '98,0%', '95,0%'). TimeC 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.
If GainMode was set to 'frame', the run-time can be extremely long for large values of Gain1 or Gain2, because the values for the gains' table are determined by a simple binary search.
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.
initialization image.
1. system matrix parameter.
Default value: 0.7
Suggested values: 0.65, 0.7, 0.75
Typical range of values: 0.05 ≤ Syspar1 ≤ 1.0
Recommended increment: 0.05
2. system matrix parameter.
Default value: 0.7
Suggested values: 0.65, 0.7, 0.75
Typical range of values: 0.05 ≤ Syspar2 ≤ 1.0
Recommended increment: 0.05
Gain type.
Default value: 'fixed'
List of values: 'fixed', 'frame'
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
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
Threshold adaptation.
Default value: 'on'
List of values: 'off', 'on'
Foreground/background threshold.
Default value: 7.0
Suggested values: 3.0, 5.0, 7.0, 9.0, 11.0
Recommended increment: 0.2
Number of statistic data sets.
Default value: 10
Suggested values: 5, 10, 20, 30
Typical range of values: 1 ≤ StatNum
Recommended increment: 5
Confidence constant.
Default value: 3.25
Suggested values: 4.30, 3.25, 2.82, 2.62
Recommended increment: 0.01
Restriction: 0.0 < ConfidenceC
Constant for decay time.
Default value: 15.0
Suggested values: 10.0, 15.0, 20.0
Recommended increment: 5.0
Restriction: 0.0 < TimeC
ID of the BgEsti data set.
* 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)
create_bg_esti returns 2 (H_MSG_TRUE) if all parameters are correct.
set_bg_esti_params, close_bg_esti
Foundation
| Operators |