Efficient adaptive density estimation per image pixel for the task of background subtraction

By Zoran Zivkovic, F. Heijden. Pattern Recognition Letters, 2006

1,641 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/a42aff222c6c8bc248a0504d063ef7334fe5a177

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity (2015, methodology): “As for the use of feedback mechanisms to adjust parameters on-the-fly in this context, many parametric methods (such as [9]–[11]) already used local variance analysis or”
Evaluation of background subtraction techniques for video surveillance (2011, background): “Kim [16, 17] 3 2004 multimodal (codeword) color, luminance pixel yes DM Zivkovic 4 [ 29 ] 2006 multimodal (Gaussian) color pixel yes DM”
ToxTrac: A fast and robust software for tracking organisms (2017, background): “March 29, 2017 14 - The scale factors are introduced in the camera matrix parameters fx and fy. The camera matrix for the example values is shown in equation 11. 10 0 0 9.5 00 0 0 1 M ªº «» «» «»¬ , (4) 3. Calibration screen Figure 6. Calibration screen, preview. 1: Select a calibration sequence. Th”
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011, methodology): “Probability Zivkovic [146], Tavakkoli et al.”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, methodology): “An adaptive scheme for KDE model update was proposed in [96], where the volumes”
ViBE: A powerful random technique to estimate the background in video sequences (2009, background): “Index Terms— Surveillance, Pattern recognition, Signal analysis, Video signal processing”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, methodology): “30 Thierry Bouwmans, Belmar Garcia-Garcia Experimental results [293] done on the dataset UnderwaterChangeDetection.eu [293] show that GSM [294] gives better performance than MOG-EM [176], Zivkovic- Heijden GMM (also called ZHGMM) [430] and EA-KDE (also called KNN) [429].”
ViBe: A Universal Background Subtraction Algorithm for Video Sequences (2011, background): “Other authors [53], [58] incorporate two temporal submodels to handle both”
A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos (2014, methodology): “DPGrimsonGMMBGS Gaussian Mixture Model [38] MixtureOfGaussianV1BGS Gaussian Mixture Model [25] MixtureOfGaussianV2BGS Gaussian Mixture Model [49] DPZivkovicAGMMBGS Gaussian Mixture Model [49] LBMixtureOfGaussians Gaussian Mixture Model [6]”
Changedetection.net: A new change detection benchmark dataset (2012, methodology): “Two fairly old, but frequently-cited, methods: KDE-based estimation by Elgammal et al. [8] and GMM by Stauffer and Grimson [24], as well as 5 improved versions of these methods: self-adapting GMM by KaewTraKulPong [12], improved GMM by Zivkovic and van der Heijden [28], block-based GMM by Dora (RECT”
Learning with structured sparsity (2009, methodology): “The background subtracted images are obtained with the software [14].”
Soccer video and player position dataset (2014, methodology): “Some of the results can be seen in Figure 7 where we have used the Zivkovic background subtraction algo-rithm [11].”