Nonparametric background generation

Liu, Yazhou; Yao, Hongxun; Gao, Wen; Chen, Xilin; Zhao, Debin

doi:10.1016/j.jvcir.2007.01.003

Cited by 52 publications

(28 citation statements)

References 19 publications

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“…Tables 1 and 2 show the number addition of the SDGD filter in the BGS techniques. Then the values of recall, precision, and F-score were calculated based on mathematical equations (10)- (12). Based on the findings shown in Tables 1 and 2, we can see that the number of TPs has increased significantly, which proves that our technique is able to detect more of the compound blob than the original methods.…”

Section: Resultsmentioning

confidence: 86%

“…Prior research on background subtraction (BGS) used several parametric BGS techniques, such as running average [2][3][4], running Gaussian average [5][6][7], approximate median filter [7,8], and Gaussian Mixture Model [9][10][11]. These parametric techniques determine the foreground and update the subsequent background based on the distribution of intensity value [12]. Aside from these techniques, other studies have introduced nonparametric models that detect foreground and background based on the intensity of statistical properties [13].…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Background Subtraction Techniques Using a Second Derivative in Gradient Direction Filter

Rahman

Hussain

Zaki

et al. 2013

Journal of Electrical and Computer Engineering

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A new approach was proposed to improve traditional background subtraction (BGS) techniques by integrating a gradient-based edge detector called a second derivative in gradient direction (SDGD) filter with the BGS output. The four fundamental BGS techniques, namely, frame difference (FD), approximate median (AM), running average (RA), and running Gaussian average (RGA), showed imperfect foreground pixels generated specifically at the boundary. The pixel intensity was lesser than the preset threshold value, and the blob size was smaller. The SDGD filter was introduced to enhance edge detection upon the completion of each basic BGS technique as well as to complement the missing pixels. The results proved that fusing the SDGD filter with each elementary BGS increased segmentation performance and suited postrecording video applications. Evidently, the analysis using Fscore and average accuracy percentage proved this, and, as such, it can be concluded that this new hybrid BGS technique improved upon existing techniques.

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Section: Resultsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Enhancement of Background Subtraction Techniques Using a Second Derivative in Gradient Direction Filter

Rahman

Hussain

Zaki

et al. 2013

Journal of Electrical and Computer Engineering

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“…Comparative analysis is compared with Top-Hat, TDLMS [14], nonparametric background method [15], anisotropic background prediction (ABP) method [16], single Gaussian (SG) [17], fuzzy running average (FRA) [18], and mixed of Gaussian (MoG) [19]. The three indicators MSE, SSIM, and GSNR are used to evaluate the background prediction effect of the infrared images.…”

Section: Background Prediction Results and Analysismentioning

confidence: 99%

“…A 5 × 5 "square" structure is adopted for Top-Hat. The settings for other methods are referenced from the literature [14][15][16][17][18][19]. The experimental results are listed from Tables 2-5.…”

Section: Background Prediction Results and Analysismentioning

confidence: 99%

“…Therefore, they can adapt to any unknown data distribution. For instance, Liu et al [15,22] adopt the model described by influencing factors to describe the changes of the background and then deduce the most reliable background status with the potentially distributed local extremism, so as to find out the point gathering most intensive data in the density distribution of data. This model is robust, able to adapt to the background under the scene that is chaotic and incompletely static, but contains small disturbance.…”

Section: Background Estimationmentioning

confidence: 99%

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Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

Fan

Zhang

et al. 2017

Mathematical Problems in Engineering

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In order to detect infrared (IR) dim and small targets in a strong clutter background, a method based on local energy center of sequential image is proposed. This paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HOC). Finally, on the basis of image preprocessing, the paper constructs a sequential image energy center detection algorithm that integrates the neighborhood, continuity, area, and energy and other motion characteristics of the target. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HOC significantly increased the signal-noise ratio of images; when the signal-noise ratio (SNR) is lower than 2.5 dB, the proposed method could effectively eliminate noise and detect targets.

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