Merge pull request #493 from kiranpradeep:bg_segm_documentation_fix

Correcting bgsegm module descriptions.  (#493)

* Correcting bgsegm module descriptions. The algorithm implementation doesn't have multi target tracking as mentioned in original paper. it only does foreground/background segmentation.

* Removing opencv_ from heading

Removing opencv_ from description

modules/README.md

@@ -10,7 +10,7 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_<r
 
 - **aruco**: ArUco and ChArUco Markers -- Augmented reality ArUco marker and "ChARUco" markers where ArUco markers embedded inside the white areas of the checker board.
 
-- **bgsegm**: Background Segmentation -- Improved Adaptive Background Mixture Model and use for real time human tracking under Variable-Lighting Conditions.
+- **bgsegm**: Background segmentation algorithm combining statistical background image estimation and per-pixel Bayesian segmentation.
 
 - **bioinspired**: Biological Vision -- Biologically inspired vision model: minimize noise and luminance variance, transient event segmentation, high dynamic range tone mapping methods.
 

modules/bgsegm/README.md

 Improved Background-Foreground Segmentation Methods 
 ===================================================
 
-1. Adaptive Background Mixture Model for Real-time Tracking
-2. Visual Tracking of Human Visitors under Variable-Lighting Conditions.
\ No newline at end of file
+This algorithm combines statistical background image estimation and per-pixel Bayesian segmentation. It[1] was introduced by Andrew B. Godbehere, Akihiro Matsukawa, Ken Goldberg in 2012. As per the paper, the system ran a successful interactive audio art installation called “Are We There Yet?” from March 31 - July 31 2011 at the Contemporary Jewish Museum in San Francisco, California.
+
+It uses first few (120 by default) frames for background modelling. It employs probabilistic foreground segmentation algorithm that identifies possible foreground objects using Bayesian inference. The estimates are adaptive; newer observations are more heavily weighted than old observations to accommodate variable illumination. Several morphological filtering operations like closing and opening are done to remove unwanted noise. You will get a black window during first few frames.
+
+References
+----------
+[1]: A.B. Godbehere, A. Matsukawa, K. Goldberg. Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. American Control Conference. (2012), pp. 4305–4312
\ No newline at end of file

modules/bgsegm/src/bgfg_gmg.cpp

@@ -41,7 +41,7 @@
 //M*/
 
 /*
- * This class implements an algorithm described in "Visual Tracking of Human Visitors under
+ * This class implements a particular BackgroundSubtraction algorithm described in "Visual Tracking of Human Visitors under
  * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
  * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
  *