Background subtraction model based on color and depth cues

Fernandez-Sanchez, Enrique J.; Rubio, Leonardo; Díaz, Javier; Ros, Eduardo

doi:10.1007/s00138-013-0562-5

Cited by 42 publications

(22 citation statements)

References 44 publications

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“…The disparity information provided as a benchmark dataset 70 is used in [13]. Figure 20.8 shows an example of input with three types of depth images taken from the dataset described in [7]. Figure 20.8 shows an example of input with three types of depth images taken from the dataset described in [7].…”

Section: Disparity Estimationmentioning

confidence: 99%

“…This benchmark dataset [7] contains a set of real video sequences including depths information of each sequence. The dataset contains four challenging scenes called suitecase, crossing, labdoor, and LCD screen.…”

Section: Disparity Benchmark Dataset (Dbd)mentioning

confidence: 99%

“…Block diagram of DEOR-PCA for background subtraction in[13]. 10 Suitecase sequence[7]. From top to bottom, input with disparity images, sparse component, thresholded mask, and ground truth with integrated binary mask.…”

mentioning

confidence: 99%

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Stochastic RPCA for Background/Foreground Separation

Javed¹,

Oh²,

Bouwmans³

et al. 2016

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

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Section: Disparity Estimationmentioning

confidence: 99%

Section: Disparity Benchmark Dataset (Dbd)mentioning

confidence: 99%

See 1 more Smart Citation

Stochastic RPCA for Background/Foreground Separation

Javed¹,

Oh²,

Bouwmans³

et al. 2016

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

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“…Researchers often have the same idea, F. Sanchez [7] apply the codebook model algorithm to 4-channel scene that pixel value is . Dong Tian [8] propose a depth-weighted group-wise PCA (DG-PCA) approach that is formulated as a weighted -norm PCA problem with depth-based group sparse being introduced.…”

Section: Introductionmentioning

confidence: 99%

An improved mixture of Gaussians model based on color and depth data

Wu¹,

Liu²,

Liang³

2017

Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 20

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Abstract. In the traditional background subtraction algorithms based on color, in the special critical situation, such as face the challenges of shadows, color camouflage, illumination changes, they can't achieve good results. But depth data can solve such problems effectively, because depth sensor operating normally in the scene. We proposed an improved mixture of Gaussian model algorithm based on color and depth data in this paper, and use a new decision-making method to fuse results. In order to ensure the stability of the model, we have special treatment of the ADO area. We test our method in three datasets, the results show that proposed approach obtain good results.

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“…In another way, Fernandez-Sanchez et al [5] propose a depth-extended Codebook model which fuses range and color information, as well as a post-processing mask fusion stage to get the best of each feature. Results are presented with a complete dataset of stereo images.…”

mentioning

confidence: 99%