* Background/foreground segmentation *
\****************************************************************************************/
-#define CV_BG_MODEL_FGD 0
-#define CV_BG_MODEL_MOG 1
-#define CV_BG_MODEL_FGD_SIMPLE 2
+/* We discriminate between foreground and background pixels
+ * by building and maintaining a model of the background.
+ * Any pixel which does not fit this model is then deemed
+ * to be foreground.
+ *
+ * At present we support two core background models,
+ * one of which has two variations:
+ *
+ * o CV_BG_MODEL_FGD: latest and greatest algorithm, described in
+ *
+ * Foreground Object Detection from Videos Containing Complex Background.
+ * Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
+ * ACM MM2003 9p
+ * http://muq.org/~cynbe/bib/foreground-object-detection-from-videos-containing-complex-background.pdf
+ *
+ * o CV_BG_MODEL_FGD_SIMPLE:
+ * A code comment describes this as a simplified version of the above,
+ * but the code is in fact currently identical. (Cynbe 2008-05-25)
+ *
+ * o CV_BG_MODEL_MOG: "Mixture of Gaussians", older algorithm, described in
+ *
+ * Moving target classification and tracking from real-time video.
+ * A Lipton, H Fujijoshi, R Patil
+ * Proceedings IEEE Workshop on Application of Computer Vision pp 8-14 1998
+ * http://www.vision.cs.chubu.ac.jp/04/pdf/VSAM02.pdf
+ *
+ * Learning patterns of activity using real-time tracking
+ * C Stauffer and W Grimson August 2000
+ * IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8):747-757
+ * http://people.csail.mit.edu/people/stauffer/Home/_papers/vsam-pami-tracking.ps
+ *
+ * Additional background may be found on the Wiki page
+ *
+ * http://opencvlibrary.sourceforge.net/VideoSurveillance
+ *
+ * which in particular recommends the Intel semi-popular overview article
+ *
+ * Computer Vision Workload Analysis: Case Study of Video Surveillance Systems
+ * Chen et al, Intel Technology Journal V09:02 , 2005 12p
+ * http://developer.intel.com/technology/itj/2005/volume09issue02/art02_computer_vision/vol09_art02.pdf
+ *
+ * which has both a good overview of the blobtracker software in particular,
+ * and also many references to introductory (and advanced) papers on computer vision.
+ *
+ */
+
+
+#define CV_BG_MODEL_FGD 0
+#define CV_BG_MODEL_MOG 1 /* "Mixture of Gaussians". */
+#define CV_BG_MODEL_FGD_SIMPLE 2
struct CvBGStatModel;
/*
Interface of ACM MM2003 algorithm
- (Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
- "Foreground Object Detection from Videos Containing Complex Background. ACM MM2003")
*/
-/* default paremeters of foreground detection algorithm */
+/* Default parameters of foreground detection algorithm: */
#define CV_BGFG_FGD_LC 128
#define CV_BGFG_FGD_N1C 15
#define CV_BGFG_FGD_N2C 25
#define CV_BGFG_FGD_N1CC 25
#define CV_BGFG_FGD_N2CC 40
-/* BG reference image update parameter */
+/* Background reference image update parameter: */
#define CV_BGFG_FGD_ALPHA_1 0.1f
/* stat model update parameter
- 0.002f ~ 1K frame(~45sec), 0.005 ~ 18sec (if 25fps and absolutely static BG) */
+ * 0.002f ~ 1K frame(~45sec), 0.005 ~ 18sec (if 25fps and absolutely static BG)
+ */
#define CV_BGFG_FGD_ALPHA_2 0.005f
/* start value for alpha parameter (to fast initiate statistic model) */
#define CV_BGFG_FGD_BG_UPDATE_TRESH 0.5f
+/* See the above-referenced Li/Huang/Gu/Tian paper
+ * for a full description of these background-model
+ * tuning parameters.
+ *
+ * Nomenclature: 'c' == "color", a three-component red/green/blue vector.
+ * We use histograms of these to model the range of
+ * colors we've seen at a given background pixel.
+ *
+ * 'cc' == "color co-occurrence", a six-component vector giving
+ * RGB color for both this frame and preceding frame.
+ * We use histograms of these to model the range of
+ * color CHANGES we've seen at a given background pixel.
+ */
typedef struct CvFGDStatModelParams
{
- int Lc, N1c, N2c, Lcc, N1cc, N2cc, is_obj_without_holes, perform_morphing;
- float alpha1, alpha2, alpha3, delta, T, minArea;
+ int Lc; /* Quantized levels per 'color' component. Power of two, typically 32, 64 or 128. */
+ int N1c; /* Number of color vectors used to model normal background color variation at a given pixel. */
+ int N2c; /* Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c. */
+ /* Used to allow the first N1c vectors to adapt over time to changing background. */
+
+ int Lcc; /* Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64. */
+ int N1cc; /* Number of color co-occurrence vectors used to model normal background color variation at a given pixel. */
+ int N2cc; /* Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc. */
+ /* Used to allow the first N1cc vectors to adapt over time to changing background. */
+
+ int is_obj_without_holes;/* If TRUE we ignore holes within foreground blobs. Defaults to TRUE. */
+ int perform_morphing; /* Number of erode-dilate-erode foreground-blob cleanup iterations. */
+ /* These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1. */
+
+ float alpha1; /* How quickly we forget old background pixel values seen. Typically set to 0.1 */
+ float alpha2; /* "Controls speed of feature learning". Depends on T. Typical value circa 0.005. */
+ float alpha3; /* Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1. */
+
+ float delta; /* Affects color and color co-occurrence quantization, typically set to 2. */
+ float T; /* "A percentage value which determines when new features can be recognized as new background." (Typically 0.9).*/
+ float minArea; /* Discard foreground blobs whose bounding box is smaller than this threshold. */
}
CvFGDStatModelParams;
/*
Interface of Gaussian mixture algorithm
- (P. KadewTraKuPong and R. Bowden,
+
"An improved adaptive background mixture model for real-time tracking with shadow detection"
- in Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001.")
+ P. KadewTraKuPong and R. Bowden,
+ Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
+ http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
*/
+/* Note: "MOG" == "Mixture Of Gaussians": */
+
#define CV_BGFG_MOG_MAX_NGAUSSIANS 500
/* default parameters of gaussian background detection algorithm */
projects / opencv / blobdiff