GraphBGS: Background Subtraction via Recovery of Graph Signals

Giraldo, Jhony H.; Bouwmans, Thierry

doi:10.48550/arxiv.2001.06404

Cited by 2 publications

(3 citation statements)

References 47 publications

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“…Besides, semi-supervised networks were also designed to be extended to unseen videos. For example, GraphBGS [66] and GraphBGS-TV [67] are based on the reconstruction of graph signals and semi-supervised learning algorithm, MSK [68] is based on a combination of offline and online learning strategies, and HEGNet [71] combines propagation-based and matching-based methods for semi-supervised video moving object detection.…”

Section: Related Workmentioning

confidence: 99%

“…Existing DNN models are mostly supervised approaches based on 2D convolutional neural networks (CNNs) [33]- [50], 3D CNNs [51]- [56], 2D separable CNNs [57], or generative adversarial networks (GANs) [58]- [63]. Besides, unsupervised GANs [64], [65] and semisupervised networks are also proposed [66]- [73]. It demonstrates that the DNNs can automatically extract spatial low-, mid-, and high-level features as well as temporal features, which turn out to be very helpful in MOD problems.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection

Hou¹,

Liu

Ling

et al. 2021

IEEE Access

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Advances in moving object detection have been driven by the active application of deep learning methods. However, many existing models render superior detection accuracy at the cost of high computational complexity and slow inference speed. This fact has hindered the development of such models in mobile and embedded vision tasks, which need to be carried out in a timely fashion on a computationally limited platform. In this paper, we propose a super-fast (inference speed-154 fps) and lightweight (model size-1.45 MB) end-to-end 3D separable convolutional neural network with a multi-input multi-output (MIMO) strategy named "3DS_MM" for moving object detection. To improve detection accuracy, the proposed model adopts 3D convolution which is more suitable to extract both spatial and temporal information in video data than 2D convolution. To reduce model size and computational complexity, the standard 3D convolution is decomposed into depthwise and pointwise convolutions. Besides, we proposed a MIMO strategy to increase inference speed, which can take multiple frames as the network input and output multiple frames of detection results. Further, we conducted the scene dependent evaluation (SDE) and scene independent evaluation (SIE) on the benchmark CDnet2014 and DAVIS2016 datasets. Compared to stateof-the-art approaches, our proposed method significantly increases the inference speed, reduces the model size, meanwhile achieving the highest detection accuracy in the SDE setup and maintaining a competitive detection accuracy in the SIE setup.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection

Hou¹,

Liu

Ling

et al. 2021

IEEE Access

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“…Giraldo and Bouwmans [17] found that the term (L + I) in the Sobolev norm in Eqn. (7) has a better condition number than L, they used the following theorems: Theorem 1 Let Ψ ∈ R N ×N be a perturbation matrix.…”

Section: Sobolev Norm Reconstruction Of Time-varying Graph Signalsmentioning

confidence: 99%

On the Minimization of Sobolev Norms of Time-Varying Graph Signals: Estimation of New Coronavirus Disease 2019 Cases

Giraldo¹,

Bouwmans²

2020

Preprint

Self Cite

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The mathematical modeling of infectious diseases is a fundamental research field for the planning of strategies to contain outbreaks. The models associated with this field of study usually have exponential prior assumptions in the number of new cases, while the exploration of spatial data has been little analyzed in these models. In this paper, we model the number of new cases of the Coronavirus Disease 2019 (COVID-19) as a problem of reconstruction of time-varying graph signals.To this end, we proposed a new method based on the minimization of the Sobolev norm in graph signal processing. Our method outperforms state-of-the-art algorithms in two COVID-19 databases provided by Johns Hopkins University. In the same way, we prove the benefits of the convergence rate of the Sobolev reconstruction method by relying on the condition number of the Hessian associated with the underlying optimization problem of our method.

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GraphBGS: Background Subtraction via Recovery of Graph Signals

Cited by 2 publications

References 47 publications

A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection

A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection

On the Minimization of Sobolev Norms of Time-Varying Graph Signals: Estimation of New Coronavirus Disease 2019 Cases

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