Video Foreground Detection Based on Symmetric Alpha-Stable Mixture Models

Bhaskar, Harish; Mihaylova, Lyudmila; Achim, Alin

doi:10.1109/tcsvt.2010.2051282

Cited by 37 publications

(17 citation statements)

References 18 publications

(36 reference statements)

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“…In addition of this, the model also displays higher robustness to illumination changes. For more detailed review of the proposed CBS-SαS technique kindly refer to [45]. In Figure 16, the edge silhouette of the target is also isolated and presented.…”

Section: Results On Moving Camera Sequencesmentioning

confidence: 99%

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Articulated human body parts detection based on cluster background subtraction and foreground matching

2013

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Detecting people or other articulated objects and localizing their body parts is a challenging computer vision problem as their movement is unpredictable under circumstances of partial and full occlusions. In this paper, a framework for human body parts tracking in video sequences using a self-adaptive cluster background subtraction (CBS) scheme is proposed based on a Gaussian mixture model (GMM) and foreground matching with rectangular pictorial structures. The efficiency of the designed human body parts tracking framework is illustrated over various real-world video sequences.

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Section: Results On Moving Camera Sequencesmentioning

confidence: 99%

“…In [45], a more detailed quantitative comparison of the CBS-SαS technique has been reported. In summary, it has been proven that the proposed CBS-SαS technique is capable to isolating targets by suppressing noise and clutter enabling the detection process to remain more robust and reliable.…”

Section: Quantitative Analysis Of Background Subtractionmentioning

confidence: 99%

Articulated human body parts detection based on cluster background subtraction and foreground matching

2013

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“…Cited by Abdullah et al (2013a) Cited by Abdullah et al (2013c) Cited by Alberton et al (2013) Cited by Ale et al (2013) Cited by Ali et al (2015) Cited by Amrein and Künsch (2012) Cited by Anai et al (2006) Cited by Andreychenko et al (2011) Cited by Andreychenko et al (2012) Cited by Andrieu et al (2010) Cited by Angius and Horváth (2011) Cited by Arnold et al (2014) Cited by Ashyraliyev et al (2009) Cited by Babtie and Stumpf (2017) Cited by Backenköhler et al (2016) Cited by Baker et al (133, 2010) Cited by Baker et al (2011) Cited by Baker et al (2013) Cited by Baker et al (2015) Cited by Banga and Canto (2008) Cited by Barnes et al (2011) Cited by Bayer et al (2015) Cited by Berrones et al (2016) Cited by Besozzi et al (2009) Cited by Bhaskar et al (2010) Cited Bogomolov et al (2015) Cited by Bouraoui et al (2015) Cited by Farza et al (2016) Cited by Boys et al (2008) Cited by Bronstein et al (2015) Cited by Brunel et al (2014) Cited by Busetto and Buhmann (2009) Cited by Camacho et al (2018) Cited by Balsa-Canto et al (2008) Cited by Carmi et al (2013) Cited by Cazzaniga et al (2015) Cited by Cedersund et al (2016) Cited by Ceska et al (2014) Cited by Ceška et al (2017) Cited by…”

Section: Abdullah Et Al (2013b)mentioning

confidence: 99%

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

2019

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The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

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“…In Ref. 17 20 and a mixture of asymmetric Gaussian distributions 21 have been employed to enhance the robustness and flexibility of mixture modeling in real scenarios, respectively. They can handle the dynamic backgrounds well.…”

Section: Parametric Modelsmentioning

confidence: 99%

Improved visual background extractor using an adaptive distance threshold

Han¹,

Wang

2014

J. Electron. Imaging

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Abstract. Camouflage is a challenging issue in moving object detection. Even the recent and advanced background subtraction technique, visual background extractor (ViBe), cannot effectively deal with it. To better handle camouflage according to the perception characteristics of the human visual system (HVS) in terms of minimum change of intensity under a certain background illumination, we propose an improved ViBe method using an adaptive distance threshold, named IViBe for short. Different from the original ViBe using a fixed distance threshold for background matching, our approach adaptively sets a distance threshold for each background sample based on its intensity. Through analyzing the performance of the HVS in discriminating intensity changes, we determine a reasonable ratio between the intensity of a background sample and its corresponding distance threshold. We also analyze the impacts of our adaptive threshold together with an update mechanism on detection results. Experimental results demonstrate that our method outperforms ViBe even when the foreground and background share similar intensities. Furthermore, in a scenario where foreground objects are motionless for several frames, our IViBe not only reduces the initial false negatives, but also suppresses the diffusion of misclassification caused by those false negatives serving as erroneous background seeds, and hence shows an improved performance compared to ViBe.

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Video Foreground Detection Based on Symmetric Alpha-Stable Mixture Models

Cited by 37 publications

References 18 publications

Articulated human body parts detection based on cluster background subtraction and foreground matching

Articulated human body parts detection based on cluster background subtraction and foreground matching

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

Improved visual background extractor using an adaptive distance threshold

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