Motion background modeling based on context-encoder

Qu, Zhenshen; Yu, Shimeng; Fu, Mengyu

doi:10.1109/icaipr.2016.7585207

Cited by 12 publications

(11 citation statements)

References 19 publications

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“…Deep learning models has a remarkable ability in background generation to cover various dynamics of outdoor environments with a series of layers. A context-encoder [31] was proven to be feasible for modelling the background of a motion-based video by learning visual features of scene context to construct the overall scene of a video. 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.…”

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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“…It allows information to be propagated within activations of each feature map. Experiments provided by Qu et al [151] are limited but convincing.…”

Section: Deep Auto Encoder Network (Dae)mentioning

confidence: 96%

“…Qu et al [151] employed a context-encoder network for a motion-based background generation method by removing the moving foreground objects and learning the feature. After removing the foreground, a context-encoder is also used to predict the missing pixels of the empty region, and to generate a background model of each frame.…”

Section: Deep Auto Encoder Network (Dae)mentioning

confidence: 99%

“…Since this work, even larger and deeper networks have been trained with the progress made by the storage for Big Data and by the GPUs for deep learning. For the field of background/foreground separation, DNNs were applied with success 1) for background generation [67,151,211,212,213], 2) for background subtraction [4,13,22,37,113], 3) foreground detection enhancement [220], 4) for ground-truth generation [204], and 5) for learned deep spatial features [108,143,166,167,222]. More practically, Restricted Boltzman Machine (RBM) was employed by Guo and Qi [67] and Xu et al [211] for background generation in order to further achieve moving object detection by background subtraction.…”

mentioning

confidence: 99%

“…More practically, Restricted Boltzman Machine (RBM) was employed by Guo and Qi [67] and Xu et al [211] for background generation in order to further achieve moving object detection by background subtraction. In a similar man-ner, Xu et al [212,213] used deep auto-encoder networks to achieve the same task while Qu et al [151] used context-encoder for background initialization. In another approach, Convolutional Neural Networks (CNNs) were employed for background subtraction by Braham and Droogenbroeck [22], Bautista et al [13], and Cinelli [37].…”

mentioning

confidence: 99%

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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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G-LBM: Generative Low-Dimensional Background Model Estimation from Video Sequences

Rezaei

Farnoosh

Ostadabbas

2020

Lecture Notes in Computer Science

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Motion background modeling based on context-encoder

Cited by 12 publications

References 19 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

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

G-LBM: Generative Low-Dimensional Background Model Estimation from Video Sequences

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