This page has been created in order to distribute a prototype software implementing the RGBD-SOBS and RGB-SOBS algorithms presented in :
L. Maddalena, A. Petrosino, Self-Organizing Background Subtraction Using Color and Depth Data, Multimedia Tools and Applications, 2018
Click here to download the Windows executable (WinZip compressed) together with the needed OpenCV .dll's.
Click here to download the Mac executable (Zip compressed).
If you have problems downloading, please contact lucia.maddalena "at" cnr.it; if you use the software, please cite the above mentioned paper.
Basic usage for SBM-RGBD dataset:
1) Unzip the RGBD-SOBS executable file (eventually with the Windows .dll's) into the directory holding the SBM-RGBD sequences .
2) Place estimated color background images for each sequence (to be adopted for background initialization as in Eq. (3) of ) in the related video directory. You can use those adopted in , which have been built using LabGen  over the first L=100 initial color frames (download them here).
To obtain all masks for the "Bootstrapping/adl24cam0" sequence, just type:
Usage with generic image sequences:
RGBD-SOBS <SeqName> [Parameters]
· <SeqName>: sequence name (complete path), not including frame numbers. Image sequences consist of
o color data saved in .png image files named with consecutive 6 digit numbers in the form “in<number>.png” (e.g., in000001.png) and stored in directory <SeqName>/input.
o depth data saved in .png image files named with consecutive 6 digit numbers in the form “d<number>.png” (e.g., d000001.png) and stored in directory <SeqName>/depth.
o color background initial estimate stored in directory <SeqName>.
· [parameters]: optional, including:
-RGBD #: To use depth data (1: RGBD-SOBS) or not (0: RGB-SOBS). Default: 1
-nini #: number of first sequence frame to be considered. Default: 1
-nend #: number of last sequence frame to be considered. Default: toIdx (toIdx read from file ‘temporalROI.txt’ as in )
-n #: (square root of) number of weight vectors for each pixel. Default: 3
-K #: Number of initial frames for training. Default: fromIdx-1 (fromIdx read from file ‘temporalROI.txt’ as in )
-e1 #: Distance threshold e1 for training phase (Eq. (7)). Default: 1.0
-e2 #: Distance threshold e2 for testing phase (Eq. (7)). Default: 0.008
-c1 #: Learning rate c1 for training phase (Eq. (11)). Default: 1.0
-c2 #: Learning rate c2 for testing phase (Eq. (11)). Default: 0.05
-Cw #: Size of the neighbourhood for Spatial Coherence (Eq. (6)). Default: 5
-s #: To apply shadow removal (as in ). Default: 1 (apply)
-g #: Shadow detection value for g in Eq. (5) in . Default: 0.7
-b #: Shadow detection value for b in Eq. (5) in . Default: 1.0
-tS #: Shadow detection value for tS in Eq. (5) in . Default: 0.1
-tH #: Shadow detection value for tH in Eq. (5) in . Default: 10.0
-ROI #: To use ROI.bmp mask as in . Default: 1 (do use)
-nameCE name: To use 'name' as color BG image for initialization (Eq. (3)). Default: CinitImageL.png (LabGen)
-m #: To save all background model images. Default: 0 (do not save)
-l #: To save all detection masks (color, depth, fused). Default: 0 (save only fused)
Examples of use with generic image sequences:
Provides the above information on usage.
2) RGBD-SOBS c:/Sequences/mysequence -nini 1 -nend 50 -K 10 -nameCE MyColorEstimate.png
where sequence mysequence has:
- color data saved in .png image files named in000001.png, …, in00050.png and stored in directory c:/Sequences/mysequence/input.
- depth data saved in .png image files named d000001.png, …, d00050.png and stored in directory c:/Sequences/mysequence/depth.
- color background initial estimate MyColorEstimate.png stored in directory c:/Sequences/mysequence.
This gives the moving object detection mask for frames from 11 to 50 (named bin000011.png to bin000011.png) achieved by RGBD-SOBS training the color and background models on the first 10 frames.
3) RGBD-SOBS c:/Sequences/mysequence –RGBD 0 -nini 1 -nend 50 -K 10 -nameCE MyColorEstimate.png
same as before, but without using depth information (i.e., this is RGB-SOBS).
 SBM-RGBD dataset, available at http://rgbd2017.na.icar.cnr.it/SBM-RGBDdataset.html
 L. Maddalena, A. Petrosino, A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, IEEE Transactions on Image Processing, DOI 10.1109/TIP.2008.924285, Vol. 17, no. 7, pagg. 1168-1177, July 2008.
 L. Maddalena, A. Petrosino, Self-Organizing Background Subtraction Using Color and Depth Data, Multimedia Tools and Applications, 2018.
 B. Laugraud, S. Pierard, M. Braham, M. Van Droogenbroeck, Simple median-based method for stationary background generation using background subtraction algorithms., In: New Trends in Image Analysis and Processing-ICIAP 2015 Workshops, LNCS, DOI 10.1007/978-3-319-23222-5_58, vol. 9281, pp. 477-484. Springer, 2015.
Last update: October 11, 2018.