Background Subtraction Using Running Gaussian Average and Frame Difference

Tang, Zhen; Miao, Zhenjiang; Wan, Yuanyuan

doi:10.1007/978-3-540-74873-1_50

Cited by 28 publications

(17 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tang, Z et al [11] have proposed a RGA model combined with frame differing method. The combination of these two methods reduce significant amount of wrongly detection pixel in the background model caused by small gaps and hole in the detection.…”

Section: ) Gaussian Based Methodsmentioning

confidence: 99%

“…Thresholding is a procedure to detect changes in the scene with respect to certain value, whereas data validation includes collection of techniques to reduce misclassifications pixels. This section , represents five distinctive background modellings that can be classified to three different categories; Median based- [1][2][3][4][5][6][7], Gaussian based- [8][9][10][11][12][13][14][15][16] as well as KDE based- [17][18][19] approaches. Besides; brief reviews of thresholding and data validation techniques [20][21][22][23][24][25] are also discussed.…”

Section: Iibackground Substraction Algorithmsmentioning

confidence: 99%

“…There are many works devoted to the development of background modelling algorithms for real time video processing [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] as it is the a backbone of the BGS algorithms. This paper thoroughly investigate five most popular algorithms from simple approaches to more sophisticated approaches, aiming to achieve the best comparison with respect to speed, memory consumption and accuracy.…”

Section: Iintroductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time background subtraction for video surveillance: From research to reality

Hedayati

Zaki

Hussain

2010

2010 6th International Colloquium on Signal Processing &Amp; Its Applications

View full text Add to dashboard Cite

This paper reviews and evaluates performance of few common background subtraction algorithms which are medianbased, Gaussian-based and Kernel density-based approaches. These algorithms are tested using four sets of image sequences contributed by Wallflower datasets. They are the image sequences of different challenging environments that may reflect the real scenario in video surveillances. The performances of these approaches are evaluated in terms of processing speed, memory usage as well as object segmentation accuracy. The results demonstrate that Gaussian-based approach is the best approach for real-time applications, compromising between accuracy and computational time. Besides, this paper may provide a better understanding of algorithm behaviours implemented in different situation for real-time video surveillance applications.

show abstract

Section: ) Gaussian Based Methodsmentioning

confidence: 99%

Section: Iibackground Substraction Algorithmsmentioning

confidence: 99%

Section: Iintroductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-time background subtraction for video surveillance: From research to reality

Hedayati

Zaki

Hussain

2010

2010 6th International Colloquium on Signal Processing &Amp; Its Applications

View full text Add to dashboard Cite

show abstract

“…Poor outputs from algorithms will eventually lead to system failure. Another interesting observation is that, it is very difficult to have an ideal algorithm the can fit all situations or scenarios, hence the need to have one that best fit most situations in order for effective application of image processing techniques for tracking, identification, classification, and recognition [9].…”

Section: Introductionmentioning

confidence: 99%

A Robust Median-based Background Updating Algorithm

ObedAppiah¹,

Hayfron-Acquah²

2017

IJIGSP

View full text Add to dashboard Cite

Abstract-Image processing techniques for object tracking, identification and classification have become common today as a result of improved quality of cameras as well as prices of cameras becoming cheaper and cheaper day by day. The use of cameras also make it possible for human analysis of video streams or images where it is difficult for robots or algorithms or machines to effectively deal with the images. However, the use of cameras for basic tracking and analysing do not come without challenges such as issues with sudden changes in illumination, shadows, occlusion, noise, and high computational time and space complexities of algorithms. A typical image processing task may involve several subtasks such as capturing, and pre-processing which demand high computational resources to complete. One of the main pre-processing tasks used in image processing is image segmentation which enables images to be divided into sections of interest in order to perform analysis on them. Background Subtraction is commonly used to segment images into Background and Foreground for further processing. Algorithms producing highly accurate results during this segmentation task normally demand high computation time or memory space, while algorithms that use smaller memory space and shorter time to complete this segmentation task may also suffer from limitations that may lead to undesired results at some point in time. Poor outputs from algorithms will eventually lead to system failure which must be avoided as much as possible. This paper proposes a median based background updating algorithm which determines the median of a buffer containing values that are highly correlated. The algorithm achieves this by deletingan extreme valuefrom the buffer whenever data is to be added to it.Experiments show that the method produces good results with less computational time which will make it possible to implement on devices that do not have much computation resources.

show abstract

“…The technique involves comparing or subtracting the current frames from the background frame and treating the remaining pixels as foreground [1]. 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].…”

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

View full text Add to dashboard Cite

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.

show abstract

Background Subtraction Using Running Gaussian Average and Frame Difference

Cited by 28 publications

References 8 publications

Real-time background subtraction for video surveillance: From research to reality

Real-time background subtraction for video surveillance: From research to reality

A Robust Median-based Background Updating Algorithm

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

Contact Info

Product

Resources

About