Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

Chau, Gustavo; Rodríguez, Paul

doi:10.1155/2019/7675805

Cited by 4 publications

(6 citation statements)

References 40 publications

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“…8. Incremental principal component pursuit [28] (called incPCP method for short). The recent IncPCP method proposes an extension of principal component pursuit that iteratively align the estimated background component to the current reference frame.…”

Section: T2-fgmm-um With Markov Random Fieldmentioning

confidence: 99%

“…Meanwhile, many deep learning based background subtraction works [18][19][20] have been introduced in recent years. The former including Frame difference, Gaussian Mixture Model (GMM), Geometric Multigrid (GMG) [21][22][23], Fuzzy methods [24][25][26] and RPCA methods [27,28], whereas the latter one commonly employs Yolov3 and Faster R-CNN [29][30][31]. However, the conventional technique does not perform well when the lighting changes, shadow changes, and the changes in the background due to short-term movements, which is difficult to meet the urgent needs of fall detection in complicated scenes at present.…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Chen

Wang

et al. 2020

IEEE Access

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Fall event, as one of the greatest risks to the elderly, its detection has been a hot research issue in the solitary scene in recent years. Nevertheless, most current researches are conducted in the ideal environments, without considering the challenge of complex background in real situation. Therefore, this paper aims to detect fall event detection in complex background based on visual data. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method is first used to accurately extract the moving objects in the noise background. Then, an attention guided Bi-directional LSTM model is proposed for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other stateof-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.

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Section: T2-fgmm-um With Markov Random Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Chen

Wang

et al. 2020

IEEE Access

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“…The movingcamera RoSuRe (mcRoSuRe) provides good detection results while at the same time avoiding the need for previous video synchronization. For moving and panning cameras, Chau and Rodriguez [44] designed an incremental PCP algorithm called incPCP-PTI which continuously aligns the low-rank component to the current reference frame of the camera. Based on the translational and rotational jitter invariant algorithm incPCP-TI [236], incPCP-PTI continuously estimates the alignment transformation T (•) in order to align the previous low-rank representation with the observed current frame.…”

Section: ) Extension To Moving Camerasmentioning

confidence: 99%

On the Applications of Robust PCA in Image and Video Processing

et al. 2018

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Robust PCA (RPCA) via decomposition into lowrank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing and 3D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a lowrank matrix. Moreover, most of the time experiments show that RPCA with additional spatial and/or temporal constraints often outperforms the state-of-the-art algorithms in these applications. Thus, the aim of this paper is to survey the applications of RPCA in computer vision. In the first part of this paper, we review representative image processing applications as follows: (1) lowlevel imaging such as image recovery and denoising, image composition, image colorization, image alignment and rectification, multi-focus image and face recognition, (2) medical imaging like dynamic Magnetic Resonance Imaging (MRI) for acceleration of data acquisition, background suppression and learning of inter-frame motion fields, and (3) imaging for 3D computer vision with additional depth information like in Structure from Motion (SfM) and 3D motion recovery. In the second part, we present the applications of RPCA in video processing which utilize additional spatial and temporal information compared to image processing. Specifically, we investigate video denoising and restoration, hyperspectral video and background/foreground separation. Finally, we provide perspectives on possible future research directions and algorithmic frameworks that are suitable for these applications.

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“…The Layering Denoising method [38] is a recent extension of REProCS that performs a video denoising step on the estimated background and foreground components at each iteration. Finally, the recent IncPCP-PTI method [39] proposes an extension of principal component pursuit that iteratively aligns the estimated background component to the current reference frame. See [40] for a recent survey of object detection methods designed for the moving camera setting.…”

Section: A Backgroundmentioning

confidence: 99%

“…The DECOLOR, GRASTA, Prac-ReProCS algorithms can, in principle, adapt to moving camera (i.e., dynamic subspaces) video, but, as we demonstrate, they are either not suitable for processing corrupted videos or their subspace tracking models are unable to accurately track the quickly evolving subspaces that arise from moving camera videos in practice. We also compare to the RASL [4] and IncPCP-PTI [39] methods.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Panoramic Robust PCA for Foreground–Background Separation on Noisy, Free-Motion Camera Video

Moore

Chen

Nadakuditi

2019

IEEE Trans. Comput. Imaging

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This work presents a new robust PCA method for foreground-background separation on freely moving camera video with possible dense and sparse corruptions. Our proposed method registers the frames of the corrupted video and then encodes the varying perspective arising from camera motion as missing data in a global model. This formulation allows our algorithm to produce a panoramic background component that automatically stitches together corrupted data from partially overlapping frames to reconstruct the full field of view. We model the registered video as the sum of a low-rank component that captures the background, a smooth component that captures the dynamic foreground of the scene, and a sparse component that isolates possible outliers and other sparse corruptions in the video. The low-rank portion of our model is based on a recent low-rank matrix estimator (OptShrink) that has been shown to yield superior low-rank subspace estimates in practice. To estimate the smooth foreground component of our model, we use a weighted total variation framework that enables our method to reliably decouple the true foreground of the video from sparse corruptions. We perform extensive numerical experiments on both static and moving camera video subject to a variety of dense and sparse corruptions. Our experiments demonstrate the state-of-the-art performance of our proposed method compared to existing methods both in terms of foreground and background estimation accuracy.

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Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

Cited by 4 publications

References 40 publications

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

On the Applications of Robust PCA in Image and Video Processing

Panoramic Robust PCA for Foreground–Background Separation on Noisy, Free-Motion Camera Video

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