L. Maddalena, A. Petrosino, “Background Subtraction for Moving Object Detection in RGB-D Data: A Survey”, MDPI Journal of Imaging, 2018.
1. Stereo Cameras (3 papers)
M. Harville, G. Gordon, J. Woodfill, “Foreground segmentation using adaptive mixture models in color and depth”, Workshop on Detection and Recognition of Events in Video, Vancouver, Canada, July 2001.
M. Harville, G. Gordon, J. Woodfill, “Adaptive background subtraction using color and depth”, IEEE International Conference on Image Processing, ICIP 2001, Thessaloniki, Greece, October 2001.
M. Harville, “A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models”, European Conference on Computer Vision, ECCV 2002, Copenhagen, Denmark, May 2002.
2. Time of Flight (ToF) (7 papers)
D. Silvestre, “Video surveillance using a time-of-light camera”, Master Thesis, Informatics and Mathematical Modelling, University of Denmark, 2007.
B. Langmann, K. Hartmann, O. Loffeld, “Depth Assisted Background Subtraction for Color Capable ToF-Cameras”, International Conference on Image and Video Processing and Computer Vision, IVPCV 2010, pages 75-82, Orlando, USA, July 2010,
B. Langmann, S. Ghobadi, K. Hartmann, O. Loffeld, “Multi-Model Background Subtraction using Gaussian Mixture Models”, Symposium on Photogrammetry Computer Vision and Image Analysis, PCV 2010, pages 61-66, 2010.
A. Stormer, M. Hofmann, G. Rigoll, "Depth gradient based segmentation of overlapping foreground objects in range images", IEEE Conference on Information Fusion, pages 1-4, 2010.
J. Leens, O. Barnich, S. Pierard, M. Van Droogenbroeck, J. Wagner, "Combining color, depth, and motion for video segmentation", Computer Vision Systems, pages 104-113, 2009.
L. Hu, L. Duan, X. Zhang, J. Yang, "Moving Object Detection Based on the Fusion of Color and Depth Information", Journal of Electronics and Information Technology, Volume 36, No. 9, pages 2047-2051, September 2014.
J. Giacomantone, L. Violini, L. Lorenti, “Background Subtraction for Time of Flight Imaging”, Journal of Computer Science and Technology, Volume 17, Number 2, October 2017.
3. Microsoft Kinect (31 papers)
M. Camplani, L. Salgado, “Background Foreground segmentation with RGB-D Kinect data: an efficient combination of classifiers”, Journal on Visual Communication and Image Representation, 2013.
M. Camplani, T. Mantecon, L. Salgado, "Depth-Color Fusion Strategy for 3D scene modeling with Kinect", IEEE Transaction on Cybernetics, 2013.
M. Camplani, C. Blanco, L. Salgado, F. Jaureguizar, N. García, "Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers", Pattern Recognition Letters, 2013.
M. Camplani, C. Blanco, L. Salgado, F. Jaureguizar, N. García, "Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction", Machine Vision and Applications, 2014.
C. Blanco, T. Mantecon, M. Camplani, F. Jaureguizar, L. Salgado, N. García, "Foreground Segmentation in Depth Imagery Using Depth and Spatial Dynamic Models for Video Surveillance Applications", Sensors 2014, 2014.
M. Camplani, A. Blasco, D. Berjon, L. Salgado, F. Moran, "Real-time RGB-D data processing on GPU architecture", IEEE International on Design and Architectures for Signal and Image Processing, DASIP 2013, October 2013.
E. Fernandez-Sanchez, J. Diaz, E. Ros, “Background Subtraction Based on Color and Depth Using Active Sensors”, Sensors, Volume 13, pages 8895-8915, 2013.
E. Fernandez-Sanchez, L. Rubio, J. Diaz, E. Ros, "Background subtraction model based on color and depth cues", Machine Vision and Applications, 2014.
J. Gallego, M. Pardas, “Region Based Foreground Segmentation Combining Color and Depth Sensors via Logarithmic Opinion Pool Decision”, Journal of Visual Communication and Image Representation, April 2013.
K. Greff, A. Brandao, S., Krauss, D. Stricker, E. Clua, “A Comparison Between Background Subtraction Algorithms using a Consumer Depth Camera”, International Conference on Computer Vision Theory and Applications , 2012
S. Ottonelli,P. Spagnolo, P. Mazzeo, M. Leo, "Foreground segmentation by combining color and depth images", Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, June 2013.
S. Ottonelli, P. Spagnolo, P. Mazzeo, M. Leo, "Improved Video Segmentation with Color and Depth using a Stereo Camera", IEEE International Conference on Industrial Technology, ICIT 2013, February 2013.
C. Spampinato, S. Palazzo, D. Giordano, “Kernel Density Estimation Using Joint Spatial-Color-Depth Data for Background Modeling”, International Conference on Pattern Recognition, ICPR 2014, 2014.
V. Nguyen, H. Vu, T. Tran, "An efficient combination of RGB and depth for background subtraction", Some Current Advanced Researches on Information and Computer Science in Vietnam, pages 49-63, 2015.
Y. Song, S. Noh, J. Yu, C. Park, B. Lee, “Background Subtraction based on Gaussian Mixture Models using Color and Depth Information”, International Conference on Control, Automation and Information Sciences, ICCAIS 2014, pages 117-120, December 2014.
Z. Liang, X. Liu, H. Liu, W. Chen, "A refinement framework for background subtraction based on color and depth data", IEEE International Conference on Image Processing, ICIP 2016, 2016.
G. Moya-Alcover, A. Elgammal, A. Jaume-i-Capo, J. Varona,"Modelling depth for nonparametric foreground segmentation using RGBD devices", Preprint, September 2016.
G. Moya-Alcover, A. Jaume-i-Capo, J. Varona, “Dealing with sequences in the RGBDT space”, Preprint, 2018.
J. Murgia, C. Meurie, Y. Ruichek, "An Improved Colorimetric Invariants and RGB-Depth-Based Codebook Model for Background Subtraction Using Kinect", MICAI 2014, 2014.
R. Trabelsi, I. Jabri, F. Smach, A. Bouallegue, "Efficient and Fast Multi-Modal Foreground-Background Segmentation using RGB-D Data",Pattern Recognition Letters, 2017.
J. Huang , H. Wu, Y. Gong, D. Gao, "Random Sampling-based Background Subtraction with Adaptive Multi-cue Fusion in RGBD Videos", International Congress on Image and Signal Processing, CISP 2016, 2016.
M. Liu, H. Liu, C. Chen, “Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions”, IEEE Transactions on Multimedia, December 2017.
N. Dorudian, S. Lauria, S. Swift, "Nonparametric background modelling and segmentation to detect Micro Air Vehicles (MAV) using RGB-D Sensor", International Journal of Micro Air Vehicles, 2018.
N. Dorudian, "Nonparametric pixel-wise background modelling and segmentation to detect moving object with RGB-D camera", Brunel University London, UK, July 2020.
Y. Li, "Foreground Segmentation via Fusion using for Low Illumination Scene", Preprint, 2022.
F. Yang, S. Jin, W. Ye, "An Improved Visual Background Extraction Algorithm Combining Depth Information", CMVIT 2019, 2019.
I. Houhou, "Moving Object Detection based on RGBD Information", PhD Thesis, 2023.
I. Houhou, A. Zitouni, Y. Ruichek, S. Bekhouche, M. Kas, "RGBD deep multi-scale network for background subtraction", International Journal of Multimedia Information Retrieval, Volume 11, pages 395–407, May 2022.
I. Houhou, A. Zitouni, Y. Ruichek, S. Bekhouche, "Improving ViBe-based Background Subtraction Techniques using RGBD Information", IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2022, pages 1-6, May 2022.
I. Houhou, A. Zitouni Y. Ruichek, F.Gouizi, S.Bekhouche, “Background Subtraction using a scale SuBSENSE”, International Conference on Electrical Engineering, ICEEB 2018 ,December 2018.
I. Houhou, A.Zitouni, Y. Ruichek, “Detection of Moving Objects using Codebook with Image Pyramid”, Image and Signal Processing and their Applications, ISPA 2017 December 2017.
4. IR Transmitter Receiver Cameras (1 paper)
J. Gallego, M. Pardas, “Region Based Foreground Segmentation Combining Color and Depth Sensors via Logarithmic Opinion Pool Decision”, Journal of Visual Communication and Image Representation, April 2013.