run_bg_estiT_run_bg_estiRunBgEstiRunBgEsti (Operator)

Name

run_bg_estiT_run_bg_estiRunBgEstiRunBgEsti — Estimate the background and return the foreground region.

Signature

Description

run_bg_estirun_bg_estiRunBgEstiRunBgEstiRunBgEsti adapts the background image stored in the BgEsti data set using a Kalman filter on each pixel and returns a region of the foreground (detected moving objects).

For every pixel an estimation of its grayvalue is computed using the values of the current data set and its stored background image and the current image (PresentImagePresentImagePresentImagePresentImagepresentImage). By comparison to the threshold (fixed or adaptive, see create_bg_esticreate_bg_estiCreateBgEstiCreateBgEstiCreateBgEsti) the pixels are classified as either foreground or background.

The background estimation processes only single-channel images. Therefore the background has to be adapted separately for every channel.

The background estimation should be used on half- or even quarter-sized images. For this, the input images (and the initialization image!) has to be reduced using zoom_image_factorzoom_image_factorZoomImageFactorZoomImageFactorZoomImageFactor. The advantage is a shorter run-time on one hand and a low-band filtering on the other. The filtering eliminates high frequency noise and results in a more reliable estimation. As a result the threshold (see create_bg_esticreate_bg_estiCreateBgEstiCreateBgEstiCreateBgEsti) can be lowered. The foreground region returned by run_bg_estirun_bg_estiRunBgEstiRunBgEstiRunBgEsti then has to be enlarged again for further processing.

Attention

The passed image (PresentImagePresentImagePresentImagePresentImagepresentImage) must have the same type and size as the background image of the current data set (initialized with create_bg_esticreate_bg_estiCreateBgEstiCreateBgEstiCreateBgEsti).

Execution Information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

Example (HDevelop)

* Read image for initialization:
read_image(InitImage,'xing/init')
* Initialize 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)
* Read the next image in sequence:
read_image(Image0,'xing/xing000')
* Estimate the background:
run_bg_esti(Image0,ForegroundRegion1,BgEstiHandle)
* Display the foreground region:
dev_display (ForegroundRegion1)
* Read the next image in sequence:
read_image(Image1,'xing/xing001')
* Estimate the background:
run_bg_esti(Image1,ForegroundRegion2,BgEstiHandle)
* Display the foreground region:
dev_display (ForegroundRegion2)
* etc.

Result

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

Possible Predecessors

create_bg_esticreate_bg_estiCreateBgEstiCreateBgEstiCreateBgEsti, update_bg_estiupdate_bg_estiUpdateBgEstiUpdateBgEstiUpdateBgEsti

Possible Successors

give_bg_estigive_bg_estiGiveBgEstiGiveBgEstiGiveBgEsti, update_bg_estiupdate_bg_estiUpdateBgEstiUpdateBgEstiUpdateBgEsti

See also

set_bg_esti_paramsset_bg_esti_paramsSetBgEstiParamsSetBgEstiParamsSetBgEstiParams, create_bg_esticreate_bg_estiCreateBgEstiCreateBgEstiCreateBgEsti, update_bg_estiupdate_bg_estiUpdateBgEstiUpdateBgEstiUpdateBgEsti, give_bg_estigive_bg_estiGiveBgEstiGiveBgEstiGiveBgEsti

Module

Foundation