A Self-Adjusting Approach to Change Detection Based on Background Word Consensus

St-Charles, Pierre-Luc; Bilodeau, Guillaume-Alexandre; Bergevin, Robert

doi:10.1109/wacv.2015.137

Cited by 180 publications

(124 citation statements)

References 19 publications

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“…SuBSENSE [26] .9500 (2) .8150 (3) .8180 (4) .6570 (5) .8990 (2) .8170 (2) .8260 (3) PAWCS [27] .9397 (4) .8137 (4) .8938 (2) .7764 (2) .8710 (3 Zivkovic [29] .768 (3) .704 (4) .632 (4) .620 (4) .300 (3) .321 (3) .820 (3) .829 (3) .748 (4) .638 (3) DP-GMM [23] . (6) .2075 (6) .9022 (5) .8700 (4) .646 (6) .6822 (5) .8169 (2) .6589 (4) .7480 (3) .6525 (6) [12] DP-GMM [23] .7876 (3) .7424 (5) .9298 (3) .8411 (5) .6665 (5) .6733 (6) .5675 (6) .6496 (5) .5522 (6) .7122 (5)…”

Section: Quantitative Resultsmentioning

confidence: 99%

Foreground Segmentation via Dynamic Tree-Structured Sparse RPCA

Ebadi

Izquierdo

2016

Computer Vision – ECCV 2016

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Abstract. Video analysis often begins with background subtraction which consists of creation of a background model, followed by a regularization scheme. Recent evaluation of representative background subtraction techniques demonstrated that there are still considerable challenges facing these methods. We present a new method in which we regard the image sequence as being made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse outlier matrix and solve the decomposition using our approximated Robust Principal Component Analysis method extended to handle camera motion. Our contribution lies in dynamically estimating the support of the foreground regions via a superpixel generation step, so as to impose spatial coherence on these regions, and to obtain crisp and meaningful foreground regions. These advantages enable our method to outperform state-of-the-art alternatives in three benchmark datasets.

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Section: Quantitative Resultsmentioning

confidence: 99%

Foreground Segmentation via Dynamic Tree-Structured Sparse RPCA

Ebadi

Izquierdo

2016

Computer Vision – ECCV 2016

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“…Otherwise, we have manually defined bounding boxes around moving objects and their shadows, to help discarding these areas for building the background models. The first half of images of the evaluation phase is used to estimate the global foreground models, using PAWCS [26] as an estimator. After this step, we perform the local feature/threshold selection.…”

Section: Description Of the Methodologymentioning

confidence: 99%

“…We feed a background subtraction algorithm (named "estimator" in the remainder of the paper) with a sequence of training images including foreground objects. The choice of the estimator is not critical; state-of-the-art techniques, such as SuBSENSE [25] or PAWCS [26], are good candidates to segment foreground objects. Note that a fast estimator is not necessary because there is no real-time requirement during the training phase.…”

Section: Estimation Of the Foreground: Local Vs Global Foreground Modelsmentioning

confidence: 99%

A Generic Feature Selection Method for Background Subtraction Using Global Foreground Models

Braham

Droogenbroeck

2015

Advanced Concepts for Intelligent Vision Systems

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Abstract. Over the last few years, a wide variety of background subtraction algorithms have been proposed for the detection of moving objects in videos acquired with a static camera. While much effort have been devoted to the development of robust background models, the automatic spatial selection of useful features for representing the background has been neglected. In this paper, we propose a generic and tractable feature selection method. Interesting contributions of this work are the proposal of a selection process coherent with the segmentation process and the exploitation of global foreground models in the selection strategy. Experiments conducted on the ViBe algorithm show that our feature selection technique improves the segmentation results.

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“…To address the complexity of dynamic scenes, most researchers have worked on developing sophisticated background models such as Gaussian mixture model [1], kernelbased density estimation [2] or codebook construction [3] (see [4], [5] for reviews on background subtraction). Other Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…authors have worked on other components such as postprocessing operations [6] or feedback loops to update model parameters ( [3], [7]). In contrast, the subtraction operation is rarely explored.…”

Section: Introductionmentioning

confidence: 99%

Deep background subtraction with scene-specific convolutional neural networks

Braham

Droogenbroeck

2016

2016 International Conference on Systems, Signals and Image Processing (IWSSIP)

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Abstract-Background subtraction is usually based on lowlevel or hand-crafted features such as raw color components, gradients, or local binary patterns. As an improvement, we present a background subtraction algorithm based on spatial features learned with convolutional neural networks (ConvNets). Our algorithm uses a background model reduced to a single background image and a scene-specific training dataset to feed ConvNets that prove able to learn how to subtract the background from an input image patch. Experiments led on 2014 ChangeDetection.net dataset show that our ConvNet based algorithm at least reproduces the performance of state-of-the-art methods, and that it even outperforms them significantly when scene-specific knowledge is considered.

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A Self-Adjusting Approach to Change Detection Based on Background Word Consensus

Cited by 180 publications

References 19 publications

Foreground Segmentation via Dynamic Tree-Structured Sparse RPCA

Foreground Segmentation via Dynamic Tree-Structured Sparse RPCA

A Generic Feature Selection Method for Background Subtraction Using Global Foreground Models

Deep background subtraction with scene-specific convolutional neural networks

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