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segmentation

segmentation

Properties

gfloat learning-rate Read / Write
GstSegmentationMethod method Read / Write
gboolean test-mode Read / Write

Types and Values

Object Hierarchy

    GObject
    ╰── GInitiallyUnowned
        ╰── GstObject
            ╰── GstElement
                ╰── GstBaseTransform
                    ╰── GstVideoFilter
                        ╰── GstOpencvVideoFilter
                            ╰── GstSegmentation

Description

This element creates and updates a fg/bg model using one of several approaches. The one called "codebook" refers to the codebook approach following the opencv O'Reilly book [1] implementation of the algorithm described in K. Kim, T. H. Chalidabhongse, D. Harwood and L. Davis [2]. BackgroundSubtractorMOG [3], or MOG for shorts, refers to a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. OpenCV MOG implements the algorithm described in [4]. BackgroundSubtractorMOG2 [5], refers to another Gaussian Mixture-based Background/Foreground segmentation algorithm. OpenCV MOG2 implements the algorithm described in [6] and [7].

[1] Learning OpenCV: Computer Vision with the OpenCV Library by Gary Bradski and Adrian Kaehler, Published by O'Reilly Media, October 3, 2008 [2] "Real-time Foreground-Background Segmentation using Codebook Model", Real-time Imaging, Volume 11, Issue 3, Pages 167-256, June 2005. [3] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.phpbackgroundsubtractormog [4] P. KadewTraKuPong and R. Bowden, "An improved adaptive background mixture model for real-time tracking with shadow detection", Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 2001 [5] http://opencv.itseez.com/modules/video/doc/motion_analysis_and_object_tracking.phpbackgroundsubtractormog2 [6] Z.Zivkovic, "Improved adaptive Gausian mixture model for background subtraction", International Conference Pattern Recognition, UK, August, 2004. [7] Z.Zivkovic, F. van der Heijden, "Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction", Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006.

Example launch line

1
gst-launch-1.0  v4l2src device=/dev/video0 ! videoconvert ! segmentation test-mode=true method=2 ! videoconvert ! ximagesink

Synopsis

Element Information

plugin

opencv

author

Miguel Casas-Sanchez <miguelecasassanchez@gmail.com>

class

Filter/Effect/Video

Element Pads

name

sink

direction

sink

presence

always

details

video/x-raw, format=(string)RGBA, width=(int)[ 1, 2147483647 ], height=(int)[ 1, 2147483647 ], framerate=(fraction)[ 0/1, 2147483647/1 ]

name

src

direction

source

presence

always

details

video/x-raw, format=(string)RGBA, width=(int)[ 1, 2147483647 ], height=(int)[ 1, 2147483647 ], framerate=(fraction)[ 0/1, 2147483647/1 ]

Functions

Types and Values

struct GstSegmentation

struct GstSegmentation;

Property Details

The “learning-rate” property

  “learning-rate”            gfloat

Speed with which a motionless foreground pixel would become background (inverse of number of frames).

Flags: Read / Write

Allowed values: [0,1]

Default value: 0.01


The “method” property

  “method”                   GstSegmentationMethod

Segmentation method to use.

Flags: Read / Write

Default value: Mixture-of-Gaussians segmentation (Zivkovic2004)


The “test-mode” property

  “test-mode”                gboolean

If true, the output RGB is overwritten with the calculated foreground (white color).

Flags: Read / Write

Default value: FALSE

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