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

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

doi:10.2174/1874479610801030219

Cited by 163 publications

(94 citation statements)

References 125 publications

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“…The choice of the curve parameters and profile is strictly related to the used background model. In our implementation where a Gaussian model is used, it is reasonable to consider a normalized distance of 2.5 (used for matched component identification in MoG models [26]) as the distance for which CL C is considered a very reliable classifier, reaching the maximum weight W max for ¿(x) = 2. On the other side, as (5(x) It has to be highlighted that in the case that L(x s ,t) =L bg , the weights W¡ are computed using Eq.…”

Section: Recursive Weights Selectionmentioning

confidence: 99%

“…For the depth classifier, the Gaussians have a single dimension; for the color based classifier, Gaussians have three components that are assumed to be independent, thus reducing the computational cost of the algorithm as widely considered in the literature [6,26]. Therefore, the covariance matrix £ is a diagonal matrix containing the variances of the three color components.…”

Section: í=Imentioning

confidence: 99%

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Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Camplani

Salgado

2014

Journal of Visual Communication and Image Representation

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Low cost RGB-D cameras such as the Microsoft's Kinect or the Asus's Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author's knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

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Section: Recursive Weights Selectionmentioning

confidence: 99%

Section: í=Imentioning

confidence: 99%

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Camplani

Salgado

2014

Journal of Visual Communication and Image Representation

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“…We employ a background subtraction algorithm to extract foreground regions of the captured image and define potential presence of observers. Since the camera is static, we use a Mixture of Gaussiansbased background modelling (Bouwmans et al, 2008). Each image pixel is characterized by its intensity value in RGB space.…”

Section: Dwell Timementioning

confidence: 99%

Audience Measurement of Digital Signage: Quantitative Study in Real-World Environment Using Computer Vision

Ravnik

Solina

2013

Interacting with Computers

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We present a quantitative study of digital signage audience measurement using computer vision. We developed a camera-enhanced digital signage display that acquires audience measurement metrics with computer vision algorithms. Temporal metrics of a person's dwell time, display in-view time and attention time are extracted. The system also determines demographic metrics of the gender and age group. The digital signage display was deployed in a real-world environment of a clothing boutique, where demographic and viewership data of 1294 store customers were recorded, manually verified and analysed. The analysis shows that 35% of customers specifically looked-at the display, having the average attention time of 0.7 s. Interestingly, the attention time was substantially higher for men (1.2 s) than for women (0.4 s). Age group comparison reveals that children (1-14 years) are the most responsive to the digital signage. Finally, the analysis shows that the average attention time is significantly higher when displaying the dynamic content (0.9 s) when compared with the static content (0.6 s).

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“…Automatic approaches usually assume that the camera and background are static, and a pre-captured background image is available. They try to model the background using either generative methods [6][7][8][9], or non-parametric methods [10,11]. Those pixels which are consistent with the background model are labeled as background, and the remainder are labeled as foreground.…”

Section: Introductionmentioning

confidence: 99%

Practical automatic background substitution for live video

Huang

Fang

et al. 2017

Comp. Visual Media

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In this paper we present a novel automatic background substitution approach for live video. The objective of background substitution is to extract the foreground from the input video and then combine it with a new background. In this paper, we use a color line model to improve the Gaussian mixture model in the background cut method to obtain a binary foreground segmentation result that is less sensitive to brightness differences. Based on the high quality binary segmentation results, we can automatically create a reliable trimap for alpha matting to refine the segmentation boundary. To make the composition result more realistic, an automatic foreground color adjustment step is added to make the foreground look consistent with the new background. Compared to previous approaches, our method can produce higher quality binary segmentation results, and to the best of our knowledge, this is the first time such an automatic and integrated background substitution system has been proposed which can run in real time, which makes it practical for everyday applications.

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

Cited by 163 publications

References 125 publications

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Audience Measurement of Digital Signage: Quantitative Study in Real-World Environment Using Computer Vision

Practical automatic background substitution for live video

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