Online Robust Principal Component Analysis Research Articles

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios.

Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal component analysis (RPCA), which is an improvement of principal component analysis (PCA) that separates an input matrix into the following two matrices: a low-rank matrix that is representative, in our case study, of the slowly changing background, and a sparse matrix that is representative of the foreground. RPCA is usually implemented in a non-causal batch form. To pursue a real-time application, we tested an online implementation, which, unfortunately, was affected by the presence of the target in the scene during the initialization phase. Therefore, we improved the robustness by implementing a saliency-based strategy. The advantages offered by the resulting technique, which we called "saliency-aided online moving window RPCA" (S-OMW-RPCA) are the following: RPCA is implemented online; along with the temporal features exploited by RPCA, the spatial features are also taken into consideration by using a saliency filter; the results are robust against the condition of the scene during the initialization. Finally, we compare the performance of the proposed technique in terms of precision, recall, and execution time with that of an online RPCA, thus, showing the effectiveness of the saliency-based approach.

Online Tensor Robust Principal Component Analysis

Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In this paper, we propose a new online robust PCA algorithm that preserves the multi-dimensional structure of data. Our algorithm is based on the recently proposed tensor singular value decomposition (T-SVD). We develop a convex optimization-based approach to recover the sparse component; and subsequently, update the low-rank component using incremental T-SVD. We propose an efficient tensor convolutional extension to the fast iterative shrinkage thresholding algorithm (FISTA) to produce a fast algorithm to solve this optimization problem. We demonstrate tensor-RPCA with the application of background foreground separation in a video stream. The foreground component is modeled as a sparse signal. The background component is modeled as a gradually changing low-rank subspace. Extensive experiments on real-world videos are presented and results demonstrate the effectiveness of our online tensor robust PCA.

Online Robust Principal Component Analysis With Change Point Detection

Robust principal component analysis (PCA) is a key technique for dynamical high-dimensional data analysis, including background subtraction for surveillance video. Typically, robust PCA requires all observations to be stored in memory before processing. The batch manner makes robust PCA inefficient for big data. In this paper, we develop an efficient online robust PCA method, namely, online moving window robust principal component analysis (OMWRPCA). Unlike the existing algorithms, OMWRPCA can successfully track not only slowly changing subspaces but also abruptly changing subspaces. Embedding hypothesis testing into the algorithm enables OMWRPCA to detect change points of the underlying subspaces. Extensive numerical experiments, including real-time background subtraction, demonstrate the superior performance of OMWRPCA compared with other state-of-the-art approaches.

Online Reweighted Least Squares Robust PCA

The letter deals with the problem known as robust principal component analysis (RPCA), that is, the decomposition of a data matrix as the sum of a low-rank matrix component and a sparse matrix component. After expressing the low-rank matrix component in factorized form, we develop a novel online RPCA algorithm that is based entirely on reweighted least squares recursions and is appropriate for sequential data processing. The proposed algorithm is fast, memory optimal and, as corroborated by indicative empirical results on simulated data and a video processing application, competitive to the state-of-the-art in terms of estimation performance.

Spatio-Temporal Constrained Online Layer Separation for Vascular Enhancement in X-Ray Angiographic Image Sequence

Automatic vascular enhancement is crucial to vascular structure identification in X-ray angiographic (XRA) image sequences. In this work, we propose a novel spatio-temporal constrained online layer separation (STOLS) method to achieve vascular enhancement in XRA image sequences. The proposed method integrates the motion consistency of structures into the temporal-constrained online robust principal component analysis (ORPCA) to remove quasi-static structures (e.g., bones) from the enhanced vascular images. Furthermore, smoothing technique is integrated into the spatial-constrained ORPCA to reduce motion artifacts and the noise introduced by non-uniform illumination. To make the proposed method more adaptive to various vascular structures, the spatial-constrained ORPCA is adjusted by an adaptive weight using the proportion of the vessel region in the previous frame. The performance of the proposed method is compared with five state-of-the-art subtraction methods with respect to local and global revised contrast-to-noise ratios (rCNRs) and reconstruction errors. For the proposed method, the local and global rCNRs of the final vessel layer reached 2.54 and 1.24, respectively, while the error between the original and reconstructed images from the respiratory, background, and vessel layer reached 0.0354. The proposed STOLS can enhance the angiograms in a real-time and online manner without fine-tuning parameters, and can thus be used for intra-operation diagnosis and interventional procedures of coronary artery diseases.

Malicious Corruption Resilience in PMU Data and Wide-Area Damping Control

This paper presents a framework for malicious corruption resilience in phasor measurement unit (PMU) data and a methodology for applying this in wide-area damping control. The problem of detecting corruptions in raw PMU measurements is formulated as a compressed sensing problem and the compromised signals are recovered using an lp-norm (0 <; p <; 1)-based online robust principal component analysis (RPCA) algorithm. The performance of the proposed method has been compared for different patterns of corruption with an l1-norm-based RPCA algorithm. The effectiveness of using the proposed data preprocessing architecture for correcting raw PMU feedback signals corrupted by missing data attack was demonstrated for closed-loop wide-area power oscillation damping control in 16-machine, 5-area New England-New York system.

Compressive Online Robust Principal Component Analysis via - Minimization.

This paper considers online robust principal component analysis (RPCA) in time-varying decomposition problems such as video foreground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of data vectors (e.g., frames) into sparse and low-rank components. Different from conventional batch RPCA, which processes all the data directly, our approach considers a small set of measurements taken per data vector (frame). Moreover, our algorithm can incorporate multiple prior information from previous decomposed vectors via proposing an - minimization method. At each time instance, the algorithm recovers the sparse vector by solving the - minimization problem-which promotes not only the sparsity of the vector but also its correlation with multiple previously recovered sparse vectors-and, subsequently, updates the low-rank component using incremental singular value decomposition. We also establish theoretical bounds on the number of measurements required to guarantee successful compressive separation under the assumptions of static or slowly changing low-rank components. We evaluate the proposed algorithm using numerical experiments and online video foreground-background separation experiments. The experimental results show that the proposed method outperforms the existing methods.

Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow

In the context of video background–foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method operates on a small set of measurements taken per frame, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-ℓ1 minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background–foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.

Deepsea video descattering

This paper presents a new “marine snow” removal method for deepsea videos based on robust temporal-spatial decomposition. For deepsea videos, the contents of adjacent frames are almost identical or change very little except for the rapidly moving “marine snow” as well as noise, indicating that there exists high temporal-spatial correlation between the successive frames. Based on this observation, we first robustly approximate the deepsea video to recover its background using online robust principal component analysis in a sub-video-by-sub-video manner. Since the structure information of background cannot be well preserved during the background modeling, we further extract such information from the approximation error to compensate the obtained background, which is also formulated as a constrained convex optimization problem. The experimental results demonstrate that our proposed method can achieve comparable or even better results than the state of the art approach.

Low Latency and High Quality Two-Stage Human-Voice-Enhancement System for a Hose-Shaped Rescue Robot

[abstFig src='/00290001/19.jpg' width='300' text='Human-voice enhancement system for a hose-shaped robot' ] This paper presents the design and implementation of a two-stage human-voice enhancement system for a hose-shaped rescue robot. When a microphone-equipped hose-shaped robot is used to search for a victim under a collapsed building, human-voice enhancement is crucial because the sound captured by a microphone array is contaminated by the ego-noise of the robot. For achieving both low latency and high quality, our system combines online and offline human-voice enhancement, providingan overview first and then details on demand. The online enhancement is used for searching for a victim in real time, while the offline one facilitates scrutiny by listening to highly enhanced human voices. Our online enhancement is based on an online robust principal component analysis, and our offline enhancement is based on an independent low-rank matrix analysis. The two enhancement methods are integrated with Robot Operating System (ROS). Experimental results showed that both the online and offline enhancement methods outperformed conventional methods.

Background subtraction based on modified online robust principal component analysis

In video surveillance, camera jitter occurs frequently and poses a great challenge to foreground detection. To overcome this challenge without any additional anti-jitter preprocessing, we propose a background subtraction method based on modified online robust principal component analysis (ORPCA). We modify the original ORPCA algorithm by introducing a prior-information-based adaptive weighting parameter to make our method adapt to variation of sparsity of foreground objects among frames, which can substantially improve the accuracy of foreground detection. In detail, we utilize sparsity of our foreground detection result of the last frame as the prior information, and adaptively adjust the weighting parameter of the sparse term for the current frame. Moreover, to make the modified ORPCA applicable to foreground detection, we also reduce the dimension of input frames through representing unoverlapped blocks by their median values. Different from recent advanced methods that rely on pixel-based background models, our method utilizes the low-dimensional subspace constructed by backgrounds of previous frames to estimate background of a new input frame, and hence can well handle the camera jitter. Experimental results demonstrate that, our method achieves remarkable results and outperforms several advanced methods in coping with the camera jitter.

Image Alignment by Online Robust PCA via Stochastic Gradient Descent

Aligning a given set of images is usually conducted in batch mode manner, which not only requires large amount of memory but also adjusts all the previous transformations to register an input image. To address this issue, we propose a novel approach to image alignment by incorporating the geometric transformation into online robust principal component analysis (PCA). Instead of calculating the warp update using noisy input samples like the conventional methods, we suggest directly linearizing the object function by performing warp update on the recovered samples, which corresponds to an efficient inverse composition algorithm. Since the basis matrix is kept constant for a given sample, both the latent vector and warp update can be very efficiently computed. Moreover, we present two basis updating methods for robust PCA, including the closed-form solution and stochastic gradient descent scheme. We have conducted the extensive experiments on the real-world tasks of background subtraction with camera motion and visual tracking on the challenging video sequences, whose promising results demonstrate the efficacy of our presented approach.

Online robust principal component analysis via truncated nuclear norm regularization

Robust principal component analysis (RPCA) has been widely used to deal with high dimensional noisy data in many applications. Traditional RPCA approaches consider all the samples to recover the low dimensional subspace in a batch manner, which incur expensive storage cost and fail to update the low dimensional subspace efficiently for stream data. Thus it is urgent to develop online RPCA methods. In this paper, we propose a novel online RPCA algorithm by adopting a recently proposed truncated nuclear norm as a tighter approximation of low rank constraint. Then we decompose the objective function as a summation of sample-wise cost. And we design an efficient alternating optimization algorithm in an online manner. Experimental results show that our proposed method can achieve more accurate low dimensional subspace estimation performance compared with state-of-the-art online RPCA algorithms.

Robust algorithms for principal component analysis

In this paper, we address the issues related to the design of fuzzy robust principal component analysis (FRPCA) algorithms. The design of robust principal component analysis has been studied in the literature of statistics for over two decades. More recently Xu and Yuille proposed a family of online robust principal component analysis based on statistical physics approach. We extend Xu and Yuille's objective function by using fuzzy membership and derive improved algorithms that can extract the appropriate principal components from the spoiled data set. The difficulty of selecting an appropriate hard threshold in Xu and Yuille's approach is alleviated by replacing the threshold by an automatically selected soft threshold in FRPCA. Artificially generated data sets are used to evaluate the performance of various PCA algorithms.