A Multiscale Parametric Background Model for Stationary Foreground Object Detection

Cheng, Steven Y.; Luo, Xingzhi; Bhandarkar, Suchendra M.

doi:10.1109/wmvc.2007.1

Cited by 31 publications

(13 citation statements)

References 13 publications

(16 reference statements)

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“…Assuming that all moving objects are vehicles, the elements in the current frame that are significantly different from the background model are considered the objects of interest. Methods for building a background model include estimates based on median filters (Cucchiara et al (2003); Lo & Velastin (2001); Zhou & Aggarwal (2001)), Kalman filters (Halevy & Weinshall (1999); Wren et al (1997)), Gaussian models (Cheng et al (2007);…”

Section: Background Subtractionmentioning

confidence: 99%

Color-based Texture Image Segmentation for Vehicle Detection

Mejia-Inigo¹,

Maria²,

Hector³

2011

Image Segmentation

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Section: Background Subtractionmentioning

confidence: 99%

Color-based Texture Image Segmentation for Vehicle Detection

Mejia-Inigo¹,

Maria²,

Hector³

2011

Image Segmentation

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“…Auto Gain Control [67] Auto White Balance [68] Automatic Exposure Correction [69] Gradual Illumination Changes (TD) [1, 24, 38, 63, 70-73, 74, 75] Sudden Illumination Changes (LS) [24,61,63,65,67,68,70,71,[74][75][76][77][78][79][80][81] Bootstrapping during initialization (B) [59,82,83] Bootstrapping during running (B) [84 -88] Camouflage (C) [42,72,73,[88][89][90][91][92] Foreground Aperture (FA) [93] Moved background objects (MBO) [60, 63, 70, 74, 75, 80 ,85, 87, 88] Inserted background objects (IBO) [60,63,70,74,75,85,87,88] Multimodal background (MB) [ …”

Section: Critical Situations Referencesmentioning

confidence: 99%

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Bouwmans¹,

Baf²,

Vachon

2008

CSENG

359

127

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Abstract:Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.

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“…Statistical methods [7][8] [9] are inspired by the basic background subtraction method. These methods are proposed to extract change regions from stationary background.…”

Section: Introductionmentioning

confidence: 99%

“…Based on background subtraction, statistical methods are proposed to extract moving region from background in [7] [11]. Background statistics are dynamically updated during processing of the video signal.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection and Segmentation using Frame Differencing and Summing Technique

Thapa¹,

Sharma²,

Ghose³

2014

IJCA

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The technology of motion detection has become one of the important research areas in computer vision. In surveillance video this has got a number of applications which range from indoor and outdoor security environment, traffic control, behavior detection during spot activities and for compression of video. In this paper simple but robust moving object detection and segmentation algorithm is proposed. The algorithm is based on the background subtraction. A differencing and summing technique (DST) has been used for the moving object detection and segmentation. This method is simple and low in computational complexity as compared to traditional object identification and segmentation techniques. The experimental results show that the proposed method work efficiently in identifying and segmenting moving objects, both in indoor as well as in outdoor environment with static background.

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A Multiscale Parametric Background Model for Stationary Foreground Object Detection

Abstract: Abstract

Cited by 31 publications

References 13 publications

Color-based Texture Image Segmentation for Vehicle Detection

Color-based Texture Image Segmentation for Vehicle Detection

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Moving Object Detection and Segmentation using Frame Differencing and Summing Technique

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