Statistical modeling of complex backgrounds for foreground object detection

By Liyuan Li, Weimin Huang, Irene Yu-Hua Gu, Qi Tian. IEEE Transactions on Image Processing, 2004

1,108 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/28ea0e030e9f8ab812a94bc06812e38e4b078932

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, methodology): “216] GMM [341] Idem GMM Idem GMM RGB Headlight/Shadow Removal Intersection Ding et al. (2012) [95] CPS based GMM [95] Idem GMM Idem GMM RGB Cyber Physical System Ding et al. (2012) [95] CPS based FGD [214] Idem FGD Idem FGD RGB Cyber Physical System Alldieck (2015) [9] Zivkovic-Heijden GMM [430] Ide”
Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey (2008, methodology): “The performance is evaluated using the ROC analysis [33] or the PDR Analysis [34] or the similarity measure [35].”
Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision (2016, methodology): “Four benchmark video sequences provided by Li et al. (Li et al, 2004) were adopted in our experiments, including 9 hhttp://www.cs.cmu.edu/ ftorre/codedata.html 10 http://winsty.net/brmf.html 11 https://sites.google.com/site/yinqiangzheng/ 12 http://www.cs.cmu.edu/ deyum/Publications.htm two outdoor…”
Fast alternating linearization methods for minimizing the sum of two convex functions (2009, neutral): “We apply ALM to solve (1.6) for two videos introduced in [30].”
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization (2009, methodology): “We illustrate this idea using two examples from [29] (see Figures 2).”
Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video (2012, background): “We use the “Lobby” dataset from [16].”
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011, methodology): “The performance is evaluated using the ROC analysis [32] or the PDR Analysis [33] or the similarity measure [34].”
Weighted Schatten $p$ -Norm Minimization for Image Denoising and Background Subtraction (2015, methodology): “2) Background Subtraction : In this subsection, we test the proposed WSNM-RPCA and other competing methods, including NNM-RPCA [32], RegL1ALM [5], MoG-RPCA [37] and WNNM based RPCA, on all the nine video sequences provided by Li et al. [38] with all frames involved.”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, methodology): “[155].”
Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery (2018, methodology): “The I2R dataset [34] 14 containing 9 videos is used in our experiments.”
Personal object discovery in first-person videos (2015, neutral): “Research about first-person video processing and understanding is also discussed.”
Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey (2021, neutral): “, statistically based, methods [11], [12].”
CDnet 2014: An Expanded Change Detection Benchmark Dataset (2014, background): “Although these datasets are challenging for these applications, they only contain day-time videos with fixed background, constant illumination, few shadows and no camera jitter.”