Robust Foreground Extraction Technique Using Gaussian Family Model and Multiple Thresholds

Kim, Han Sung; Sakamoto, Ryuuki; Kitahara, Itaru; Toriyama, Tomoji; Kiyokawa, Kiyoshi

doi:10.1007/978-3-540-76386-4_72

Cited by 39 publications

(28 citation statements)

References 15 publications

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“…Li et al (2013) proposed foreground detection for static cameras using online expectation-maximisation algorithm. Kim et al (2007) extracted the foreground using generalised Gaussian family model. Shih and Huang (2013) employ scalar invariant features transform trajectories to extract the foreground for videos from moving cameras.…”

Section: Related Workmentioning

confidence: 99%

“…1 real-time foreground-background segmentation using the codebook model (RTFBSCM) (Kim et al, 2005) 2 foreground extraction technique using Gaussian family models and multiple thresholds (FEGMT) (Kim et al, 2007) 3 moving foreground object detection via robust SIFT trajectories (MFOSIFT) (Shih and Huang, 2013) 4 Illumination robust foreground detection in a video isurveillance system (IRFDVSS) (Li et al, 2013).…”

Section: Effective Foreground Extractionmentioning

confidence: 99%

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An optimised background modelling for efficient foreground extraction

Sivagami

Revathi²,

Jeganathan

2017

IJHPCN

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Nowadays, analysing videos from a surveillance system in real-time is very important for resolving the security related social issues. Foreground extraction and object detection is a vital task in video analysis. In the proposed methods background, modelling is treated as an optimisation problem and solved using particle swarm optimisation. The background is modelled at regular intervals of time for adapting the changes in the environment. Then the background subtraction is applied to the current frame with the corresponding background modelled frame to extract the foreground. Added to it the optical flow applied image is compared with the foreground extracted image to avoid the false positives (FP) and false negatives (FN). This proposed foreground extraction technique for real-time videos gives results better than the previous algorithms with respect to the quality of extraction and space complexity. L. Jeganathan is a Professor and the Dean of the School of Computing Science and Engineering, VIT University, India. Also, he heads the research team of theoretical computer science with the motivation of designing the effective computational models using cellular automata. His research interest includes theoretical computer science, natural computing and bio-inspired computing. He has completed his PhD in IIT Madras in 2009. He has published many national and international conference and journal papers.

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Section: Related Workmentioning

confidence: 99%

Section: Effective Foreground Extractionmentioning

confidence: 99%

An optimised background modelling for efficient foreground extraction

Sivagami

Revathi²,

Jeganathan

2017

IJHPCN

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“…This is a fundamental difference with the basic models for which the tolerance is fixed for every pixel. As shown by Kim et al [80], a generalized Gaussian model can also be used and Morde et al [118] have shown that a Chebychev inequality can also improve results. With this model, the detection criteria depends on how many standard deviations a color is from P.-M. JODOIN, S. PIÉRARD, Y. WANG, and M. VAN DROOGENBROECK.…”

Section: Parametric Modelsmentioning

confidence: 99%

Background Subtraction on Embedded Hardware

Jodoin¹,

Pierard

Wang³

et al. 2014

Background Modeling and Foreground Detection for Video Surveillance

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“…-Generalized GMM [167][168][169][170][171] which allows to alleviate the constraint of a strict Gaussian.…”

Section: Current and Future Developmentsmentioning

confidence: 99%

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Bouwmans¹,

Baf²,

Vachon

2008

CSENG

359

127

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Abstract:Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.

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Robust Foreground Extraction Technique Using Gaussian Family Model and Multiple Thresholds

Cited by 39 publications

References 15 publications

An optimised background modelling for efficient foreground extraction

An optimised background modelling for efficient foreground extraction

Background Subtraction on Embedded Hardware

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

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