Improving background subtraction using Local Binary Similarity Patterns

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

doi:10.1109/wacv.2014.6836059

Cited by 138 publications

(103 citation statements)

References 21 publications

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“…The second row shows the corresponding ground truth. And in the next 4 rows, there are Chromaticity [12], Large region texture (LR Texture) [11], LOB-STOR [15] and Multi-Cues [13] which are usually used to detect the foreground and remove the shadow. The first two of them are the individual shadow removal process after GMM background subtraction.…”

Section: Resultsmentioning

confidence: 99%

Inequality-Constrained RPCA for Shadow Removal and Foreground Detection

Zhang

Wang

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYState-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closedform solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.

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Section: Resultsmentioning

confidence: 99%

Inequality-Constrained RPCA for Shadow Removal and Foreground Detection

Zhang

Wang

et al. 2015

IEICE Trans. Inf. & Syst.

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“…Among these methods, ViBe [24] is a nonparameter algorithm, from which the proposed method is derived. LOB-STER [27] and Multiscale Spatiotemporal BG Model [30] 8 Advances in Multimedia are spatiotemporal background modeling algorithms, which are similar to the proposed method. EFIC [31] is a popular method in changedetection.net.…”

Section: Comparison With Other Methodsmentioning

confidence: 98%

“…The change detection results from LSBP proved efficiency against many complex algorithms. Reference [27] improved LSBP in threshold area and combined with ViBe method to detect motion. The improvement was obviously in noisy and blurred regions.…”

Section: Related Workmentioning

confidence: 99%

Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

Yang

Zhang

Wang

et al. 2017

Advances in Multimedia

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Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

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“…St-Charles and Biloteau [33] proposed an efficient method (named SuBSENSE) using a spatio-temporal Local Binary Similarity Patterns (LBSP) descriptor instead of simply relying on pixel intensities as its core component, it keeps memory usage, complexity and speed at acceptable levels for online applications. St-Charles et al also proposed another method [34] based on local binary patterns as well as color information, also called multi-Q method (multi-objectives method).…”

Section: Recent Approachesmentioning

confidence: 99%

Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection

et al. 2018

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Abstract:Moving foreground detection is a very important step for many applications such as human behavior analysis for visual surveillance, model-based action recognition, road traffic monitoring, etc. Background subtraction is a very popular approach, but it is difficult to apply given that it must overcome many obstacles, such as dynamic background changes, lighting variations, occlusions, and so on. In the presented work, we focus on this problem (foreground/background segmentation), using a type-2 fuzzy modeling to manage the uncertainty of the video process and of the data. The proposed method models the state of each pixel using an imprecise and adjustable Gaussian mixture model, which is exploited by several fuzzy classifiers to ultimately estimate the pixel class for each frame. More precisely, this decision not only takes into account the history of its evolution, but also its spatial neighborhood and its possible displacements in the previous frames. Then we compare the proposed method with other close methods, including methods based on a Gaussian mixture model or on fuzzy sets. This comparison will allow us to assess our method's performance, and to propose some perspectives to this work.

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Improving background subtraction using Local Binary Similarity Patterns

Cited by 138 publications

References 21 publications

Inequality-Constrained RPCA for Shadow Removal and Foreground Detection

Inequality-Constrained RPCA for Shadow Removal and Foreground Detection

Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection

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