Background estimation and removal based on range and color

Gordon, G.; Darrell, Trevor; Harville, M.; Woodfill, John

doi:10.1109/cvpr.1999.784721

Cited by 116 publications

(84 citation statements)

References 9 publications

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“…The overall ranking across categories RC¡ of ¡th method is computed as the mean of the single RM¡ across all the sequences. In the following paragraphs we compare the performance of the following algorithms: the proposed adaptive weighted classifier CL W ; the two weak classifiers CL C and CL D ; the MoG algorithm proposed in [18] M0G RG B-D\ and the binary combinations of the foreground masks obtained by two independent modules based on depth and color data as proposed in [19] (by using MoG) and in [20] (by using ViBe), we refer to these algorithm as M0G Bin and Vibe B ¡". Finally we adapt to the RGBD feature space the neural networks algorithm proposed in [17] (SOM) and the modified MoG algorithm proposed in [14] (MoG Z w).lt has to be noted that no post-processing stages, such as morphological filtering, are applied to the resulting foreground masks.…”

Section: Benchmark Data and Resultsmentioning

confidence: 99%

“…One of the first proposals based on both color and depth data is presented in [18]; it is an adaptation of the MoG algorithm to color and depth data obtained with a stereo device. Each background pixel is modeled as a mixture of four dimensional Gaussian distributions: three components are the color data (YUV space in this case) and the fourth one is the depth data, D. Color and depth features are considered independent and the same updating strategy of the original MoG algorithm is used to update the distribution parameters.…”

Section: Foreground/background Segmentation With Depth Datamentioning

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: Benchmark Data and Resultsmentioning

confidence: 99%

Section: Foreground/background Segmentation With Depth Datamentioning

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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“…Different extensions of this model were developed by changing the characteristics at pixel level. Gordon et al 11 represent each pixel with four components: the three color components and the depth.…”

Section: Parametric Methodsmentioning

confidence: 99%

Automatic vehicle counting system for traffic monitoring

Crouzil

Khoudour

Valiere

et al. 2016

J. Electron. Imaging

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“…However, as mentioned before, temporal inconsistency in static parts of the scene leads to salient and disturbing artifacts. This was addressed in the context of video-conferencing by algorithms that segment stereoscopic video into layers [13,20] utilizing both depth and color information. The intended application, however, is background replacement and not free viewpoint video.…”

Section: Related Workmentioning

confidence: 99%

Consistent 3D Background Model Estimation from Multi-viewpoint Videos

Tsekourakis

Mordohai

2015

2015 International Conference on 3D Vision

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We present an approach for estimating the 3D background model of a scene from a collection of synchronized videos. Unlike previous work, our method is fully automatic, does not require empty frames depicting just the background, and makes very mild assumptions about the foreground. The constraint on the cameras is that they should have sufficiently narrow baselines to enable multiview stereo matching. Using the images and primarily the depth maps as inputs, our algorithm detects potential background pixels to generate initial per-camera background models, which are then fused to form the final, consistent 3D background model. We show results on diverse video sequences captured using different camera configurations. Despite the challenges posed by the input videos, in which some parts of the background are always occluded in the images, we are able to extract accurate models of the background that are effective in foreground segmentation. This would have been impossible using conventional background subtraction methods that operate on the frames of each camera separately. Moreover, fusion makes the per-camera background models consistent.

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Background estimation and removal based on range and color

Cited by 116 publications

References 9 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

Automatic vehicle counting system for traffic monitoring

Consistent 3D Background Model Estimation from Multi-viewpoint Videos

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