Statistical Background Modeling for Foreground Detection: A Survey

Bouwmans, Thierry; Baf, Fida El; Vachon, Bertrand

doi:10.1142/9789814273398_0008

Cited by 102 publications

(61 citation statements)

References 73 publications

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“…Motion-based methods generally detect the cyclic motion of the legs and assume a static camera to identify the moving foreground [50]. Although joint segmentation and detection processes have been proposed [28] [51], person detection often considers a blob segmented from the background, over which further analysis is performed to verify whether the blob is a pedestrian.…”

Section: B Motion-based Methodsmentioning

confidence: 99%

Video-Based Human Behavior Understanding: A Survey

Borges

Conci

Cavallaro

2013

IEEE Trans. Circuits Syst. Video Technol.

188

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Abstract-Understanding human behaviors is a challenging problem in computer vision that has recently seen important advances. Human behavior understanding combines image and signal processing, feature extraction, machine learning and 3D geometry. Application scenarios range from surveillance to indexing and retrieval, from patient care to industrial safety and sports analysis. Given the broad set of techniques used in videobased behavior understanding and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguously key terms and discuss links among fundamental building blocks ranging from human detection to action and interaction recognition. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects of video-based human behavior understanding, available datasets for experimentation and comparisons, and important open research issues.

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Section: B Motion-based Methodsmentioning

confidence: 99%

Video-Based Human Behavior Understanding: A Survey

Borges

Conci

Cavallaro

2013

IEEE Trans. Circuits Syst. Video Technol.

188

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“…The principle of these techniques consists in building a background model for each pixel and then to compare the model to the current value of a pixel. Several papers review background subtraction techniques [3,4,6,8,15]. Except for the exact form of their model, background subtraction techniques also differentiate in the way they update the model, and how they compare a pixel value to the model.…”

Section: Introductionmentioning

confidence: 99%

Background subtraction: Experiments and improvements for ViBe

Droogenbroeck

Paquot

2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

217

131

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Motion detection plays an important role in most video based applications. One of the many possible ways to detect motion consists in background subtraction. This paper discusses experiments led for a particular background subtraction technique called ViBe. This technique models the background with a set of samples for each pixel and compares new frames, pixel by pixel, to determine if a pixel belongs to the background or to the foreground.In its original version, the scope of ViBe is limited to background modeling. In this paper, we introduce a series of modifications that alter the working of ViBe, like the inhibition of propagation around internal borders or the distinction between the updating and segmentation masks, or process the output, for example by some operations on the connected components. Experimental results obtained for video sequences provided on the workshop site validate the improvements of the proposed modifications.

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“…Most of them rely on motions of the scene and try to model the background using statistical approaches. 6 Because of the need to motion information, these approaches require sequential frames captured from a scene, rather than a single image. For detecting background pixels, these approaches mostly rely on color histogram information as well as motion of the objects.…”

Section: Introductionmentioning

confidence: 99%

Computationally efficient background subtraction in the light field domain

2014

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In this paper we present a novel approach for depth estimation and background subtraction in light field images. our approach exploits the regularity and the internal structure of the light field signal in order to extract an initial depth map of the captured scene and uses the extracted depth map as the input to a final segmentation algorithm which finely isolates the background in the image.Background subtraction is natural application of the light field information since it is highly involved with depth information and segmentation. However many of the approaches proposed so far are not optimized specifically for background subtraction and are highly computationally expensive. Here we propose an approach based on a modified version of the well-known Radon Transform and not involving massive matrix calculations. It is therefore computationally very efficient and appropriate for real-time use.Our approach exploits the structured nature of the light field signal and the information inherent in the plenoptic space in order to extract an initial depth map and background model of the captured scene. We apply a modified the Radon transform and the gradient operator to horizontal slices of the light field signal to infer the initial depth map. The initial depth estimated are further refined to a precise background using a series of depth thresholding and segmentation in ambiguous areas.We test on method on various types real and synthetic of light field images. Scenes with different levels of clutter and also various foreground object depth have been considered in the experiments. The results of our experiments show much better computational complexity while retaining comparable performance to similar more complex methods.

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Statistical Background Modeling for Foreground Detection: A Survey

Cited by 102 publications

References 73 publications

Video-Based Human Behavior Understanding: A Survey

Video-Based Human Behavior Understanding: A Survey

Background subtraction: Experiments and improvements for ViBe

Computationally efficient background subtraction in the light field domain

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