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GPU based GMM segmentation of kinect data

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Abdenour Amamra, Tarek Mouats, Nabil Aouf

Computer Vision and Pattern Recognition · Submitted 28 Oct 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Test results show its robustness against illumination change, shadows and reflections.

how it is stated · asserted 1.00 read the record →

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arXiv ID2110.14934 (version 1)
Pith IDESXF5AZN, Pith's permanent record of this paper and its review
CitationAbdenour Amamra, Nabil Aouf, Tarek Mouats. "GPU based GMM segmentation of kinect data." arXiv:2110.14934 (2021). https://pith.science/paper/ESXF5AZN
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,680 input tokens.

What the abstract itself states, as probabilities: claims first 0.23 · states limitation 0.06 · human participants 0.08 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.86 · compares with prior results 0.06 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper presents a novel approach for background/foreground segmentation of RGBD data with the Gaussian Mixture Models (GMM). We first start by the background subtraction from the colour and depth images separately. The foregrounds resulting from both streams are then fused for a more accurate detection. Our segmentation solution is implemented on the GPU. Thus, it works at the full frame rate of the sensor (30fps). Test results show its robustness against illumination change, shadows and reflections.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper presents a novel approach for background/foreground segmentation of RGBD data with the Gaussian Mixture Models (GMM). contribution 1.00 0.85 0.15 asserted 0.99
s1 We first start by the background subtraction from the colour and depth images separately. method 1.00 0.84 0.02 asserted 0.95
s2 The foregrounds resulting from both streams are then fused for a more accurate detection. method 1.00 0.85 0.06 asserted 0.99
s3 Our segmentation solution is implemented on the GPU. method 0.95 0.89 0.05 asserted 1.00
s4 Thus, it works at the full frame rate of the sensor (30fps). result 0.91 0.87 0.11 asserted 1.00
s5 Test results show its robustness against illumination change, shadows and reflections. result 0.99 0.94 0.38 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.96.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper presents a novel approach for background/foreground segmentation of RGBD data with the Gaussian Mixture Models (GMM). We first start by the background subtraction from the colour and depth images separately. The foregrounds resulting from both streams are then fused for a more accurate detection. Our segmentation solution is implemented on the GPU. Thus, it works at the full frame rate of the sensor (30fps). Test results show its robustness against illumination change, shadows and reflections.

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