Non-parametric statistical background modeling for efficient foreground region detection

Tavakkoli, Alireza; Bebis, George; Nicolescu, Mircea

doi:10.1007/s00138-008-0134-2

Cited by 58 publications

(44 citation statements)

References 13 publications

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“…6-8, it can be found that the proposed method has outperformed the MoG method in these selected situations. Table I shows the average of the similarity measures using " (21)," which is evaluated on 20 randomly selected frames of each sequence. The quantitative evaluation agrees with the conclusions from the visual observation of the experimental results.…”

Section: Resultsmentioning

confidence: 99%

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A Novel Approach in Video Scene Background Estimation

Kashani¹,

Seyedin²,

Yazdi³

2010

IJCTE

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Abstract-This paper presents a novel method for background estimation in a video sequence from the function estimation point of view. The proposed algorithm, called Kernel-based Background Learning (KBL), is designed based on kernel machine joint with learning schemes. In order to estimate background using KBL algorithm, we first interpret foreground samples as outliers relative to the background ones and so propose an Outlier Separator (OS). Then, the obtained results of OS algorithm are employed in the KBL method in order to train and estimate background in each pixel. Experimental results show the high accuracy and effectiveness of the proposed method in background estimation and foreground detection for the scenes including moving backgrounds, camera shakes, and non-empty backgrounds.Index Terms-Background estimation, outlier separator, kernel-based background learning.

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Section: Resultsmentioning

confidence: 99%

“…The model was able to learn sudden illumination changes. However, slow model training speed and sensitivity to model selection and initialization are problems of these methods [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach in Video Scene Background Estimation

Kashani¹,

Seyedin²,

Yazdi³

2010

IJCTE

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“…Although the material surface may have a weak texture, algorithms for texture analysis and identification [38][39][40][41], besides their computational cost, cannot be applied here as texture is often distorted at the boundaries of the defect. Moreover, some texture may easily be confounded with the defect itself (cf.…”

Section: Background Subtraction (Binarization)mentioning

confidence: 99%

Automatic Defect Classification on a Production Line

Borghese

Fomasi

2015

Intell Ind Syst

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We describe here a novel defect classification system that works in real-time on the images of material running on the production line, provided by a video-inspection module. The classifier is constituted of a two-levels hierarchical architecture: a set of adequate features are extracted from the image first; the defect type is then identified from them. Statistical analysis allows greatly reducing the number of features, leaving only the most significant ones. A particular version of multi-class boosting has been developed for labeling: differently from its original version, it accepts only one label for each image, the true one, and does not require the rank for the other classes. Nevertheless, the classifier is able to produce a valid rank of the defect with respect to all the classes, information that can be used to achieve an identification rate of the dangerous defects very close to 100 % on a real data set.

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“…In this work, a d-variate Gaussian has been chosen as the kernel estimator function, K H , due to its continuity, differentiability and locality properties [6]. In order to reduce the computational requirements, a diagonal bandwidth matrix has been applied.…”

Section: A Background Modelingmentioning

confidence: 99%

Segmentation-tracking feedback approach for high-performance video surveillance applications

Cuevas

del-Blanco

García

et al. 2010

2010 IEEE Southwest Symposium on Image Analysis &Amp; Interpretation (SSIAI)

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Abstract-Here, a novel and efficient feedback system for moving object segmentation and tracking is proposed. Through the use of non-parametric background-foreground modeling, moving objects are correctly detected in unfavorable situations such as dynamic backgrounds or illumination changes. After detection, objects are tracked by an original multiobject Bayesian tracking algorithm, which achieves satisfactory results under partial and total occlusions. Updating the previously detected foreground data from the information provided by the tracker, the foreground modeling is improved, reducing the color similarity problem.

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Non-parametric statistical background modeling for efficient foreground region detection

Cited by 58 publications

References 13 publications

A Novel Approach in Video Scene Background Estimation

A Novel Approach in Video Scene Background Estimation

Automatic Defect Classification on a Production Line

Segmentation-tracking feedback approach for high-performance video surveillance applications

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