Background recovery from video sequences via online motion-assisted RPCA

Yang, Jiaoru; Yang, Jingyu; Yang, Xin; Yue, Huanjing

doi:10.1109/vcip.2016.7805552

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…For detailed qualitative comparisons, the proposed method is evaluated by comparing it with the online RPCA method [32] , OMA-RPCA method [40] , and OTNNR method [10] . These methods are state-of-the-art techniques for online background modeling.…”

Section: Resultsmentioning

confidence: 99%

“…where the MSE is the mean square error of the real background and the reconstruction background. [40] 74.1 0.023 OTNNR [10] 75.7 0.027 Online RPCA [32] 76.3 0.020…”

Section: Quantitative Evaluations and Analysismentioning

confidence: 99%

“…Recently, X. Ye [42] proposed a motion-assisted matrix restoration (RMAMR) model for the separation of background objects from the foreground objects, in which the dense motion fields were incorporated into the framework of the RPCA. J. Yang [40] proposed an online motion-assisted RPCA model for back ground recovery from video sequences. This method is more efficient for memory and is scalable for the long video sequences, which are weighted by motion information.…”

Section: Introductionmentioning

confidence: 99%

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Background modeling from video sequences via online motion-aware RPCA

2021

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Background modeling of video frame sequences is a prerequisite for computer vision applications. Robust principal component analysis(RPCA), which aims to recover low rank matrix in applications of data mining and machine learning, has shown improved background modeling performance. Unfortunately, The traditional RPCA method considers the batch recovery of low rank matrix of all samples, which leads to higher storage cost. This paper proposes a novel online motion-aware RPCA algorithm, named OM-RPCAT, which adopt truncated nuclear norm regularization as an approximation method for of low rank constraint. And then, Two methods are employed to obtain the motion estimation matrix, the optical flow and the frame selection, which are merged into the data items to separate the foreground and background. Finally, an efficient alternating optimization algorithm is designed in an online manner. Experimental evaluations of challenging sequences demonstrate promising results over state-of-the-art methods in online application.

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Section: Resultsmentioning

confidence: 99%

“…where the MSE is the mean square error of the real background and the reconstruction background. [40] 74.1 0.023 OTNNR [10] 75.7 0.027 Online RPCA [32] 76.3 0.020…”

Section: Quantitative Evaluations and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Background modeling from video sequences via online motion-aware RPCA

2021

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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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Background recovery from video sequences via online motion-assisted RPCA

Cited by 2 publications

References 9 publications

Background modeling from video sequences via online motion-aware RPCA

Background modeling from video sequences via online motion-aware RPCA

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

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