Background modelling based on generative unet

Tao, Ye; Palasek, Petar; Zhou, Ling; Patras, Ioannis

doi:10.1109/avss.2017.8078483

Cited by 15 publications

(14 citation statements)

References 11 publications

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“…Xu et al [32] used an adaptive Restricted Boltzmann Machine, which performs approximate learning with an aim of capturing the temporal correlation between adjacent video frames to construct background. Tao et al [33] showed an augmented version of a generative architecture BM-Unet with unsupervised training to produce background image via a probabilistic heat map of the colour values. The power of the deep learning models also gave attention to background subtraction and foreground detection enhancement.…”

Section: Methodsmentioning

confidence: 99%

TensorMoG: A Tensor-Driven Gaussian Mixture Model with Dynamic Scene Adaptation for Background Modelling

Chung

Phan

et al. 2020

Sensors

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Decades of ongoing research have shown that background modelling is a very powerful technique, which is used in intelligent surveillance systems, in order to extract features of interest, known as foregrounds. In order to work with the dynamic nature of different scenes, many techniques of background modelling adopted the unsupervised approach of Gaussian Mixture Model with an iterative paradigm. Although the technique has had much success, a problem occurs in cases of sudden scene changes with high variation (e.g., illumination changes, camera jittering) that the model unknowingly and unnecessarily takes into account those effects and distorts the results. Therefore, this paper proposes an unsupervised, parallelized, and tensor-based approach that algorithmically works with entropy estimations. These entropy estimations are used in order to assess the uncertainty level of a constructed background, which predicts both the present and future variations from the inputs, thereby opting to use either the incoming frames to update the background or simply discard them. Our experiments suggest that this method is highly integrable into a surveillance system that consists of other functions and can be competitive with state-of-the-art methods in terms of processing speed.

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Section: Methodsmentioning

confidence: 99%

TensorMoG: A Tensor-Driven Gaussian Mixture Model with Dynamic Scene Adaptation for Background Modelling

Chung

Phan

et al. 2020

Sensors

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“…Background modeling Unet (BM-Unet) [31] is a background reconstruction model which also uses a U-net network but is trained without any supervision or ground-truth data and can perform both fixed and dynamic background reconstruction. For fixed background reconstruction, it is trained with pairs of random images sampled from one frame sequence.…”

Section: Related Workmentioning

confidence: 99%

Fast and Accurate Background Reconstruction Using Background Bootstrapping

Sauvalle

Fortelle

2022

J. Imaging

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The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using acceleration with a graphics processing unit (GPU) and a Python implementation.

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“…Tao et al [187] proposed an unsupervised deep learning model for Background Modeling called BM-Unet. This method is based on the generative architecture U-Net [158] which for a given frame (input) provides the corresponding background image (output) with a probabilistic heat map of the color values.…”

Section: U-netmentioning

confidence: 99%

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

et al. 2019

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Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. For this, we first surveyed the methods used background initialization, background subtraction and deep learned features. Then, we discuss the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset.

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Background modelling based on generative unet

Cited by 15 publications

References 11 publications

TensorMoG: A Tensor-Driven Gaussian Mixture Model with Dynamic Scene Adaptation for Background Modelling

TensorMoG: A Tensor-Driven Gaussian Mixture Model with Dynamic Scene Adaptation for Background Modelling

Fast and Accurate Background Reconstruction Using Background Bootstrapping

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

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