Effective background modelling and subtraction approach for moving object detection

Liu, Wei; Yu, Hongjie; Yuan, Huai; Zhao, Hong; Xu, Xiaowei

doi:10.1049/iet-cvi.2013.0242

Cited by 22 publications

(8 citation statements)

References 43 publications

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“…Table 10 includes the detected regions with their PCC factor details for objects 1, 2 and 3 depending to tables (8)(9) and equation (5). The given PCC results in table (10), exhibited that using the Prewitt Edge technique to detect the samples used objects is achieved best results in compared with the Sobel edge technique, the differences of the PCC between Sobel and Prewitt techniques are appeared more clearly in figure 25.…”

Section: Fig 24: Processing Time Comparison For Edges Techniquesmentioning

confidence: 89%

“…The background is very important for any moving object detection and considered as a key of any video surveillance or automatic video analyses [10], the background is modeled using many captured images then the average operation has to be applied to obtained the background modeling, Figure (18), the foreground are also modeled and combined with the background, Figures (19-21)), this mechanism is applied for all the three used object and the following results as shown in table(3) are obtained.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

See 1 more Smart Citation

A Comparison between Moving Object Detection Methods Including a Novel Algorithm Used for Industrial Line Application

Abdilatef¹

2016

IJCA

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Section: Fig 24: Processing Time Comparison For Edges Techniquesmentioning

confidence: 89%

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Comparison between Moving Object Detection Methods Including a Novel Algorithm Used for Industrial Line Application

Abdilatef¹

2016

IJCA

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“…The second approach is the calculation of the optical flow [3], [4] which provides all information about the movement, but the real-time implementation is difficult and calculation of flow being generally slow. The background subtraction approaches start by modeling the background(model of static scene), it can detect the most precise foreground [5][6] [ [24][25][26][27][28][29][30][31], , but it has many limitations like sensor noise (noise of acquisition and digitization) and management of homogeneous areas when the luminance difference between two moments is less than a threshold.…”

Section: Introductionmentioning

confidence: 99%

A block-based background model for moving object detection

Elharrouss

Abbad

Moujahid

et al. 2017

ELCVIA

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Detecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incoming frame. Our background frame is generated by collecting the blocks background candidates. The block candidate selection is based on probability density function (pdf) computation. After that, we compute the absolute difference between the background frame and each frame of sequence. A noise filter is applied using the Structure/Texture decomposition in order to minimize the noise caused by background subtraction operation. The binary motion mask is formed using an adaptive threshold that was deduced from the weighted mean and variance calculation. To assure the correspondence between the current frame and the background frame, an adaptation of background model in each incoming frame is realized. After comparing results obtained from the proposed method to other existing ones, it was shown that our approach attains a higher degree of efficacy.Key Words: Motion detection, Background subtraction, Background model, Background update, Video surveillance. IntroductionMotion detection is a paramount study field of computer vision. Its purpose is to extract the moving objects at time t in a video captured using a stationary camera. The motion detection is used for many applications. Among these applications there are video surveillance, human-machine interaction, the recognition of sign language specific to robotics applications and many others.The approaches mainly used for motion detection can be classified into three categories: the time difference methods, the analysis of optical flow, and the background subtraction methods. For the first one, the time difference being the calculation of the difference between two or more consecutive images in order to extract the moving area [1], [2], but the problem in this approach is that the detected objects are incomplete and poorly presented. The second approach is the calculation of the optical flow [3], [4] which provides all information about the movement, but the real-time implementation is difficult and calculation of

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“…On the basis of pixel for modelling background, background model can be classified into pixel-based model [7][8][9][10][11][12] and region-based model. [13][14][15][16][17][18] The pixelbased methods model observed scenes as a set of independent pixel processes, while the region-based methods represent background model by using inter-pixel relations. Among these methods, one of the most widely used methods for background modelling is the Gaussian Mixture Model (GMM).…”

Section: Introductionmentioning

confidence: 99%

“…In Panda and Meher, 17 a novel fuzzy colour difference histogram was proposed to represent the background, it exhibited better performance over many background subtraction methods in terms of classification accuracy metrics. The hierarchical method was presented in Liu et al 18 In which, the first level is a pixel-wise background modelling method for coarse detection, while the second level is a refined detection based on a block-wise foreground validation. The method can improve the foreground detection results, yet it is still difficult for dynamic background.…”

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confidence: 99%

Moving object detection based on confidence factor and CSLBP features

Zhang

Duan

2016

The Imaging Science Journal

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Moving object detection is a challenging task in many computer vision applications. In this paper, we propose a robust background modelling method for this task. First, the background is updated by an adaptive strategy based on Centre-symmetric Local Binary Patterns. Then, background subtraction is used for detecting moving object. Although the traditional background subtraction technique uses the difference value between the current pixel and its corresponding background pixel for objection detection, our method utilises the confidence factor to determine whether the current pixel is a background or foreground pixel. The confidence factor of the current pixel is calculated in term of the difference values of its neighbourhood pixels. In the experiments, the proposed algorithm is tested on several challenging datasets such as PETS 2009, BMC 2012 and SABS. The experimental results demonstrate that our algorithm can robustly detect moving object under various scenes. IntroductionMoving object detection, which is also called moving object segmentation, is an intelligent analysis technology to extract moving object from surveillance video sequences. It is a fundamental problem in many computer vision applications, such as video surveillance, industrial automation and intelligence transportation. 1 Although many methods have been proposed for moving object detection, it is still a challenging problem because of dynamic scenes, such as illumination changes, dynamic changes relating to swaying trees, rippling water, fleeting cloud and so on.Generally, moving object detection method can be classified into three categories, which are the inter-frame difference method, the optical flow method and the background subtraction mehod. 2,3 Compared with the inter-frame difference and the optical flow method, background subtraction is a widely used method in practical applications because of its simple realisation, fast computing speed and non-prior knowledge. The performance of background subtraction mainly depends on the background modelling technique. A robust background modelling algorithm needs to overcome background changes, which occur in outdoor scenes, such as illumination changes caused by sun moving and dynamic changes including swaying branches or water fluctuations. Therefore, how to build and maintain an adaptive background model is a key problem of this method and is still a hot research topic. [4][5][6] In the past two decades, a large number of different background modelling methods have been proposed. Due to their pervasiveness in many applications, there is no unique classifying method on the existing works of background subtraction techniques. According to the model parameters, these methods can be divided into methods with or without parametric. In terms of the features used in modelling, there are single characteristic models and hybrid characteristic models. On the basis of pixel for modelling background, background model can be classified into pixel-based model 7-12 and region-based model. [13][14][15][16]...

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Effective background modelling and subtraction approach for moving object detection

Cited by 22 publications

References 43 publications

A Comparison between Moving Object Detection Methods Including a Novel Algorithm Used for Industrial Line Application

A Comparison between Moving Object Detection Methods Including a Novel Algorithm Used for Industrial Line Application

A block-based background model for moving object detection

Moving object detection based on confidence factor and CSLBP features

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