Surveillance Video Analysis Using Compressive Sensing With Low Latency (original) (raw)
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Adaptive low rank and sparse decomposition of video using compressive sensing
2013
We address the problem of reconstructing and analyzing surveillance videos using compressive sensing. We develop a new method that performs video reconstruction by low rank and sparse decomposition adaptively. Background subtraction becomes part of the reconstruction. In our method, a background model is used in which the background is learned adaptively as the compressive measurements are processed. The adaptive method has low latency, and is more robust than previous methods. We will present experimental results to demonstrate the advantages of the proposed method.
Recursive Low-rank and Sparse Recovery of Surveillance Video using Compressed Sensing
Proceedings of the International Conference on Distributed Smart Cameras, 2014
This paper focuses on surveillance video processing using Compressed Sensing (CS). The CS measurements are used for recovery of the video frame into a low-rank background component and sparse component that corresponds to the moving object. The spatial and temporal low-rank features of the video frame, e.g., the nonlocal similar patches within the single video frame and the low-rank background component residing in multiple frames, are successfully exploited. We propose rLSDR that consists of three major components. First we develop an efficient single frame CS recovery algorithm, called NLDR, that operates on the nonlocal similarity patches within each frame to solve the low-rank optimization problem with the CS measurements constraint using Douglas-Rachford splitting method. Second, after obtaining a few NLDR recovered frames as training, a fast bilateral random projections (BRP) scheme is adopted for quick low-rank background initialization. Third, rLSDR then incorporates real-time single video frame to recursively recover the sparse component and update the background, where the proposed NLDR algorithm can also be used here for sparse component estimation. Experimental results on standard surveillance videos demonstrate that NLDR performs the best for single frame CS recovery compared with the state-of-the-art and rLSDR could successfully recover the background and sparse object with less resource consumption.
Compressive sensing based video object compression schemes for surveillance systems
Video Surveillance and Transportation Imaging Applications 2015, 2015
In some surveillance videos, successive frames exhibit correlation in the sense that only a small portion changes (object motion). If the foreground moving objects are segmented from the background they can be coded independently requiring far fewer bits compared to frame-based coding. Huang et al proposed a Compressive Sensing (CS) based Video Object Error Coding (CS-VOEC) where the objects are segmented and coded via motion estimation and compensation. Since motion estimation might be computationally intensive, encoder can be kept simple by performing motion estimation the decoder rather than at the encoder. We propose a novel CS based Video Object Compression (CS-VOC) technique having a simple encoder in which the sensing mechanism is applied directly on the segmented moving objects using a CS matrix. At the decoder, the object motion is first estimated so that a CS reconstruction algorithm can efficiently recover the sparse motion-compensated video object error. In addition to simple encoding, simulation results show our coding scheme performs on par with the state-of-the-art CS based video object error coding scheme. If the object segmentation requires more computations, we propose to deploy a distributed CS framework called Distributed Compressive Video Sensing based Video Object Compression (DCVS-VOC) wherein the object segmentation is done only for key frames.
Compressive sensing approaches for autonomous object detection in video sequences
2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2015
Video analytics requires operating with large amounts of data. Compressive sensing allows to reduce the number of measurements required to represent the video using the prior knowledge of sparsity of the original signal, but it imposes certain conditions on the design matrix. The Bayesian compressive sensing approach relaxes the limitations of the conventional approach using the probabilistic reasoning and allows to include different prior knowledge about the signal structure. This paper presents two Bayesian compressive sensing methods for autonomous object detection in a video sequence from a static camera. Their performance is compared on the real datasets with the non-Bayesian greedy algorithm. It is shown that the Bayesian methods can provide the same accuracy as the greedy algorithm but much faster; or if the computational time is not critical they can provide more accurate results.
2017
This thesis introduces the recent advances on decomposition into low-rank plus sparse matrices and tensors, as well as the main contributions to face the principal issues in moving object detection. First, we present an overview of the state-of-the-art methods for low-rank and sparse decomposition, as well as their application to background modeling and foreground segmentation tasks. Next, we address the problem of background model initialization as a reconstruction process from missing/corrupted data. A novel methodology is presented showing an attractive potential for background modeling initialization in video surveillance. Subsequently, we propose a double-constrained version of robust principal component analysis to improve the foreground detection in maritime environments for automated video-surveillance applications. The algorithm makes use of double constraints extracted from spatial saliency maps to enhance object foreground detection in dynamic scenes. We also developed tw...
Multimedia Tools and Applications, 2017
Limited memory, energy and bandwidth are the major constraints in wireless visual sensor network (WVSN). Video surveillance applications in WVSN attracts a lot of attention in recent years which requires high detection accuracy and increased network lifetime that can be achieved by reducing the energy consumption in the sensor nodes. Compressed sensing (CS) based background subtraction plays a significant role in video surveillance application for detecting the presence of anomaly with reduced complexity and energy. This paper presents a system based on CS for single and multi object detection that can detect the presence of an anomaly with higher detection accuracy and minimum energy. A novel and efficient mean measurement differencing approach with adaptive threshold strategy is proposed for detection of the objects with less number of measurements, thereby reducing transmission energy. The performance of the system is evaluated in terms of detection accuracy, transmission energy ...
Applications of Compressive Sensing to Surveillance Problems
Springer Proceedings in Mathematics & Statistics, 2014
In many surveillance scenarios, one concern that arises is how to construct an imager that is capable of capturing the scene with high fidelity. This could be problematic for two reasons: First, the optics and electronics in the camera may have difficulty in dealing with so much information; second, bandwidth constraints may pose difficulty in transmitting information from the imager to the user efficiently for reconstruction or realization. This paper is a study of the application of various compressive sensing methods to surveillance problems. It is based largely on the work of [7], with theory and algorithms presented in the same manner. We explore two of the seminal works in compressive sensing and present the key theorems and definitions from these two papers. We then survey three different surveillance scenarios and their respective compressive sensing solutions. The original contribution of this paper is the development of a distributed compressive sensing model.
Compressive sensing in video applications
2013 21st Telecommunications Forum Telfor (TELFOR), 2013
In this paper, a new approach to estimate motion parameters in compressive sensed video sequences is proposed. The proposed procedure combines sparse reconstruction algorithms and time-frequency analysis applied to µ µ µ µ-propagation signal. This concept allows providing precise velocity estimation even under a reduced number of randomly chosen video frames. The theory is applied and illustrated on synthetic and real video sequence.
Foreground Detection via Robust Low Rank Matrix Decomposition including Spatio-Temporal Constraint
Foreground detection is the first step in video surveillance system to detect moving objects. Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. In this paper, we propose to use a low-rank matrix factorization with IRLS scheme (Iteratively reweighted least squares) and to address in the minimization process the spatial connexity and the temporal sparseness of moving objects (e.g. outliers). Experimental results on the BMC 2012 datasets show the pertinence of the proposed approach.
Moving Object Detection via Robust Low Rank Matrix Decomposition with IRLS scheme
Moving object detection is a key step in video surveillance system. Recently, Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background when the camera is fixed. The background sequence is then modeled by a low-rank subspace that can gradually change over time, while the moving objects constitute the correlated sparse outliers. In this paper, we propose to use a low-rank matrix factorization with IRLS (Iteratively Reweighted Least Squares) scheme for RPCA decomposition and to address in the minimization process the spatial connexity of the pixels. Experimental results on different datasets show the pertinence of the proposed method.