Background Modelling from a Moving Camera

This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories: modelling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. We also present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms.

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“…Viswanath et al [130] concluded that a direct linear transform can be efficient, when the camera motion is just PTZ.…”

Section: Modelling Based Background Subtractionmentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

207

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“…H.Zhou et al [2] have proposed a foreground detection methodology in which the authors have tried to improvise the codebook. Viswanth et al [3] suggested and modeled an approach using non parametric background modeling. In this approach, a single Spatio Temporal Gaussian is used for modeling the back ground pixels.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, most of the background modelling techniques need to combat the challenges due to dynamic or non-static backgrounds, unexpected or steady lighting changes; motion in the object and shade, Background modelling methods should intelligently overcome such issues. To overcome these challenges, many models are presented in the literature [1][2][3][4][5][6][7][8][9][10][11], [13], [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Background Subtraction using Generalized Rayleigh Distribution

Tadiparthi¹,

Yarramalle²,

Vadaparthi³

2018

ijacsa

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Identification of the foreground objects in dynamic scenario video images is an exigent task, when compared to static scenes. In contrast to motionless images, video sequences offer more information concerning how items and circumstances change over time. Pixel based comparisons are carried out to categorize the foreground and the background based on frame difference methodology. In order to have more precise object identification, the threshold value is made static during both the cases, to improve the recognition accuracy, adaptive threshold values are estimated for both the methods. The current article also highlights a methodology using Generalized Rayleigh Distribution (GRD). Experimentation is conducted using benchmark video images and the derived outputs are evaluated using a quantitate approach.

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“…However, extraction of moving object with moving camera deals with problem like registration error, parallax effect, slow initialization, large computation memory due to the construction of the large possible field of view and time problem [14]. Some algorithms based on non-panoramic background model for moving camera segmentation [13][14][15][16][17][18] have been studied to resolve these problems but still suffer from wrong classification of motionless background pixels as a foreground pixel. In the proposed work, the problem of false labeling of background pixel as foreground pixel was resolved by calculating the motion vector of each pixel with the assumption that background pixels have negligible magnitude compared to moving pixels.…”

Section: Introductionmentioning

confidence: 99%

Dense Flow-Based Video Object Segmentation in Dynamic Scenario

Kushwaha

Prakash

Srivastava

et al. 2018

Lecture Notes in Electrical Engineering

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Segmenting object from a moving camera is a challenging task due to varying background. When camera and object both are moving, then object segmentation becomes more difficult and challenging in video segmentation. In this paper, we introduce an efficient approach to segment object in moving camera scenario. In this work, first step is to stabilize the consecutive frame changes by the global camera motion and then to model the background, non-panoramic background modeling technique is used. For moving pixel identification of object, a motion-based approach is used to resolve the problem of wrong classification of motionless background pixel as foreground pixel. Motion vector has been constructed using dense flow to detect moving pixels. The quantitative performance of the proposed method has been calculated and compared with the other state-of-the-art methods using four measures, such as average difference (AD), structural content (SC), Jaccard coefficients (JC), and mean squared error (MSE).

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Background Modelling from a Moving Camera

Cited by 28 publications

References 12 publications

New trends on moving object detection in video images captured by a moving camera: A survey

New trends on moving object detection in video images captured by a moving camera: A survey

A Novel Approach for Background Subtraction using Generalized Rayleigh Distribution

Dense Flow-Based Video Object Segmentation in Dynamic Scenario

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