An Improved Colorimetric Invariants and RGB-Depth-Based Codebook Model for Background Subtraction Using Kinect

Murgia, Julian; Meurie, Cyril; Ruichek, Yassine

doi:10.1007/978-3-319-13647-9_35

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…In [15], they further proposed another method based on Codebook algorithm which fuses range and color to remove most noise in disparity data by morphological reconstruction. Julian Murgia et al [16] described a background subtraction method that utilizes colorimetric invariance to correct mistakes caused by illumination and combined depth information as external data sources.…”

Section: A Background Subtraction With Depth Sensorsmentioning

confidence: 99%

“…We evaluate our proposed method BGSNet-D and these traditional methods on the SBM-RGBD [19] dataset. The experiments compare BGSNet-D with the state-of-the-art methods, including (1) AvgM-D [19], which only uses the depth data in RGBD2017 1 , (2) GMM [3] used in [7][8][9][10][11][12][13], (3) Codebook [4] used in [14] [16], (4) KDE [5] used in [17] [18].…”

Section: A Experimental Settingsmentioning

confidence: 99%

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Background Subtraction on Depth Videos with Convolutional Neural Networks

Liu

Dong

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Background subtraction is a significant component of computer vision systems. It is widely used in video surveillance, object tracking, anomaly detection, etc. A new data source for background subtraction appeared as the emergence of low-cost depth sensors like Microsoft Kinect, Asus Xtion PRO, etc. In this paper, we propose a background subtraction approach on depth videos, which is based on convolutional neural networks (CNNs), called BGSNet-D (BackGround Subtraction neural Networks for Depth video). The method can be used in color unavailable scenarios like poor lighting situations, and can also be applied to combine with existing RGB background subtraction methods. A preprocessing strategy is designed to reduce the influences incurred by noise from depth sensors. The experimental results on the SBM-RGBD dataset show that the proposed method outperforms existing methods on depth data, and even reaches the performance of the methods that use RGB-D data.

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Section: A Background Subtraction With Depth Sensorsmentioning

confidence: 99%

Section: A Experimental Settingsmentioning

confidence: 99%

Background Subtraction on Depth Videos with Convolutional Neural Networks

Liu

Dong

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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“…Zones that show notable differences between the current frame and the background model are deemed to correspond to moving objects. Generally, background subtraction algorithms include the Average Background Model (AVG) algorithm, the Gaussian Mixture Model (GMM) algorithm [ 6 ], the Codebook algorithm [ 7 ] and the Visual Background Extractor (ViBe) algorithm [ 8 , 9 , 10 ]. The ViBe algorithm is a fast motion detection algorithm proposed by Olivier Barnich et al [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm

Xiao-qin

Liu

Jiang

et al. 2017

Sensors

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Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in three-dimensional space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D (Red-Green-Blue and Depth) algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

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“…Zones that show notable differences between the current frame and the background model are deemed to correspond to moving objects. Generally, background subtraction algorithms include the average background model (AVG) algorithm, the GMM algorithm [5], the Codebook algorithm [6] and the ViBe algorithm [7][8][9]. The ViBe algorithm is a fast motion detection algorithm proposed by Olivier Barnich et al [7].…”

Section: Introductionmentioning

confidence: 99%

Improving Video Segmentation by Fusing Depth Cues and the ViBe Algorithm

Xiao-qin¹,

Liu²,

Jiang³

et al. 2017

Preprint

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Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

show abstract

An Improved Colorimetric Invariants and RGB-Depth-Based Codebook Model for Background Subtraction Using Kinect

Cited by 11 publications

References 20 publications

Background Subtraction on Depth Videos with Convolutional Neural Networks

Background Subtraction on Depth Videos with Convolutional Neural Networks

Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm

Improving Video Segmentation by Fusing Depth Cues and the ViBe Algorithm

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