Traditional and recent approaches in background modeling for foreground detection: An overview

Bouwmans, Thierry

doi:10.1016/j.cosrev.2014.04.001

Cited by 626 publications

(358 citation statements)

References 217 publications

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“…2. The evaluation of the segmentation map occurs on a pixel base in most benchmarks such as changedetection.net [2,10], which indicates that the spatial coherence is not the primary concern. In fact, none of the 7 metrics computed on that website depends on the temporal or spatial order of pixels.…”

Section: Methodsmentioning

confidence: 99%

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A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

Pierard

Droogenbroeck

2015

New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops

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Abstract. The quest for the best background subtraction technique is ongoing. Despite that a considerable e ort has been undertaken to develop exible and e cient methods, some elementary questions are still unanswered. One of them is the existence of an intrinsic upper bound to the performance. In fact, data are a ected by noise, and therefore it is illusory to believe that it is possible to achieve a perfect segmentation.This paper aims at exploring some intrinsic limitations of the principle of background subtraction. The purpose consists in studying the impact of several limiting factors separately. One of our conclusions is that even if an algorithm would be able to calculate a perfect background image, it is not su cient to achieve a perfect segmentation with background subtraction, due to other intrinsic limitations.

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Section: Methodsmentioning

confidence: 99%

“…The background subtraction (BGS) is a well studied problem [2,10] for which, despite the impressive amount of methods proposed in the literature so far, no satisfactory technique has been found yet (for all cases) [6,7]. This paper discusses the limits of pixel-based BGS methods.…”

Section: Introductionmentioning

confidence: 99%

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

Pierard

Droogenbroeck

2015

New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops

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“…Video degradations represent the external disturbance which affect the successive video frames [13]. Salt & pepper noise (shot noise or binary noise) can be caused by sharp and sudden disturbances in the video frames.…”

Section: Video Degradation and Suggested Filtering Techniquesmentioning

confidence: 99%

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

Ata¹,

El-Darieby²,

Elnaby³

et al. 2017

IJIGSP

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Abstract-In this paper, an enhancement based traffic video has been proposed in the state of the art of computer vision. The main target is to develop a decision making criteria for removing the most probable video degradations. Such traffic video degradations would have an adverse impact on the transportation system. In order to establish the appropriate analysis, three types of video degradations have been added to the test video; salt and pepper noise, Gaussian noise, and speckle noise, we have simulated rainy, fog, and darkness conditions for the traffic video. First of all, back ground subtraction and Kalman filter techniques have been used for detecting and tracking vehicles respectively. By using such algorithms, it would be easily to estimate average number of assigned tracks which express the efficacy of correct detection and prediction of vehicles in each frame. Furthermore, video degradations would be applied in order to studying its effect on the average number of assigned tracks which would be deviated than noiseless video. Spatial filtering system has been applied to state the most suitable filter mask which satisfy the least deviation in the average number of assigned tracks. Experimental results show that median filter satisfies the least deviation in all cases of video degradations.

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“…The background model estimates the potential background in image sequences, and the foreground model detects foreground re- 20 gions by comparing between captured frames and the estimated background. Currently, although a large number of algorithms have been proposed for background subtraction [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16], some problems remain open for a background subtrac-25 tion algorithm designed for robots. One main challenge is the varying working environment.…”

Section: Introductionmentioning

confidence: 99%

An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system

Sirlantzis

Howells

et al. 2016

Robotics and Autonomous Systems

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Please cite this article as: Y. Hu, et al., An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system, Robotics and Autonomous Systems (2016), http://dx.doi.org/10. 1016/j.robot.2016.08.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system AbstractIn this paper, we design a fast background subtraction algorithm and deploy this algorithm on a monitoring system based on NAO humanoid robot. The proposed algorithm detects a contiguous foreground via a contiguously weighted linear regression (CWLR) model. It consists of a background model and a foreground model. The background model is a regression based low rank model. It seeks a low rank background subspace and represents the background as the linear combination of the basis spanning the subspace. The foreground model promotes the contiguity in the foreground detection. It encourages the foreground to be detected as whole regions rather than separated pixels. We formulate the background and foreground model into a contiguously weighted linear regression problem. This problem can be solved efficiently via an alternating optimization approach which includes continuous and discrete variables. Given an image sequence, we use the first few frames to incrementally initialize the background subspace, and we determine the background and foreground in the following frames in an online scheme using the proposed CWLR model, with the background subspace continuously updated using the detected background information. The proposed algorithm is implemented by Python on a NAO humanoid robot based monitoring system. This system consists of a control station and a Nao robot. The Nao robot acts as a mobile probe. It captures image sequence and sends it to the control station. The control station serves as a control terminal. It sends commands to control the behaviour of Nao robot, and it processes the image data sent by Nao. This system can be used for living environment monitoring and form the basis for many vision-based applications like fall detection and scene understanding. The experimental comparisons with most recent algorithms on both benchmark dataset and NAO captures demonstrate the high effectiveness of the proposed algorithm.

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Traditional and recent approaches in background modeling for foreground detection: An overview

Cited by 626 publications

References 217 publications

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

Traffic Video Enhancement based Vehicle Correct Tracked Methodology

An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system

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