Overview

Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ.  La Rochelle, France.

A full overview of the background subtraction methods listed in this website are provided in:

Editors: T. Bouwmans, F. Porikli, B. Hörferlin, A. Vacavant.

Title: Handbook “Background modeling and Foreground Detection for video surveillance:  Traditional and Recent Approaches, Benchmarking and Evaluation".

Publisher :CRC Press, Taylor and Francis Group.

Publication Date :  July 1, 2014. (More information) [Purchase]

Further Improvements

If you would like to list your publication related to this topic on this website, please send me your publication in .pdf and I will add the reference.

Fair Use Policy

As this website gives many information that come from my research, please cite my following papers:

C. Silva, T. Bouwmans, C. Frelicot, 'An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos", VISAPP 2015, Berlin, Germany, March 2015.

C. Silva, T.  Bouwmans, C. Frelicot, "Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground Separation", International Conference on  Pattern Recognition, ICPR 2016, December 2016.

C. Pacheco, T. Bouwmans, C. Frelicot,"Superpixel-based online wagging one-class  ensemble for feature selection in foreground/background separation", Pattern Recognition Letters, 2017. 

T. Bouwmans, C. Silva, C. Marghes, M. Zitouni, H. Bhaskar, C. Frelicot, “On the Role and the Importance of Features for Background Modeling and Foreground Detection”, Computer Science Review, Volume 28, pages 26-91, May 2018.

My recent publications are available on Academia, Research Gate, Researchr, ORCID and Publication List.

Objective

The aim of this web site is to provide resources such as references (534 papers), datasets, codes and links to demonstration website for the research on robust features for background foreground separation by grouping all related researches and particularly recent advances in this field. For this, it  is organized in the following sections:

1. Features Size (45 papers)

    Pixel, Keypoint (2 papers), Superpixel (12 papers),  Block  (2 papers), Cluster (4 papers), Region (9 papers), 

    Brick (5 papers), Patch (4 papers), Semantic (6 papers)

2. Features in the Pixel Domain (360 papers)

     2.1 Crisp Features (302 papers)

           2.1.1 Intensity Features (39 papers)

            Intensity features (15 papers), IR Intensity features (24 papers),

           2.1.2 Color Features (48 papers)

                    1) Color Features in Well-Known Color Spaces (18 papers)

                     RGB (1 paper), Normalized RGB (1 paper), YUV (2 papers), HSV (1 paper), HSI  (1 paper), Luv  (1 paper), 

                     Improved HLS  (1 paper), YCrCb  (1 paper), Lab2000HL  (2 papers),  Bayer Features (1 paper), 

 Color Space Comparison (6 papers)  

                    2) Color Features in Designed Shape Color Spaces (13 papers)

                    3) Designed Illumination Invariant Color Features (6 papers)

                    4) Color Filter Array (CFA) Features (1 paper)

                    5) Statistics on Color Features (10 papers)

            2.1.3 Edge Features (23 papers)

            2.1.4 Texture Features (145 papers)

             Peripheral Pattern (3 papers), Local Pattern (96 papers),  Radial Pattern (20 papers),  

             Spatio-temporal Pattern (16 papers), Other Pattern (10 papers)

            2.1.5 Motion Feature (2 papers)

            2.1.6 Stereo Features (45 papers)

            Disparity Features (DiF) (3 papers), Depth Features (DeF) (42 papers)

    2. 2. Statistical Features (26 papers)

           Histogram of intensity (1 paper),  Histogram of color space (9 papers), Histograms of Gradient (HG) (7 paper), 

           Histograms of Figure/Ground Segementation (1 paper), Entropy (4 papers), 

           Statistical Reach Feature (2 papers), Statistical Texture (2 papers)

    2. 3. Fuzzy Features (16 papers)

            1) Fuzzy Local Histograms (7 papers)

           Local Fuzzy Color Histograms (LFCH) (6 papers), Local Fuzzy Color Difference Histograms (LFCDH) (1 paper),  

  Fuzzy Local Binary Pattern (FLBP) (1 paper)     

       2) Fuzzy Histon (FH) (3 papers),

          3) Fuzzy Correlogram (FC) (2 papers)

     4) Fuzzy Statistical Texture (FST) (3 papers)

    5) Fuzzy Local Binary Pattern (FLBP) (1 paper)

    2.4. Other Features  (16 papers)

        Frequency Features (2 papers), Phase Features (5 papers), Spatial Features (1 paper), Location Features (7 papers), Bit Planes Features (1 paper)

3. Features in a Transform Domain (32 papers)

    3.1  Frequency Domain Transform (21 papers)

    Fast Fourier Transform  Features (1 paper), Discrete Cosinus Transform Features (9 papers),  

    Wavelet Transform Features (1 paper),

    Walsh Transform Features (3 papers), Hadamar Transform Features (1 paper), 

    Gabor Transform Features (4 papers), Slant Transform Features (2 papers)

    3.2  Video Domain Transform (11 papers)

    MPEG Features (5 papers), H.264/AVC Features (3 papers), HEVC Features (3 papers)

4. Features in a Deep Learning Domain (17 papers)

    Deep Learned Features (17 papers)

5. Features Strategies (65 papers)

    Color-Edge (3 papers), Color-Texture (23 papers), Color-Stereo (26 papers), Color-Gradient-Texture (3 papers), Color-Texture-Motion (CTM) (3 papers), Color-Texture-Semantics (SIM-MFR) (1 paper), More than three features (3 papers), Bag-of-Features (3 papers)

6. Features Selection (9 papers)

7. Features Others (6 papers)

7.1 Features Reliability (4 papers)

7.2 Feature Relevance (2 papers)