14,531 research outputs found
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Background subtraction has been a fundamental and widely studied task in
video analysis, with a wide range of applications in video surveillance,
teleconferencing and 3D modeling. Recently, motivated by compressive imaging,
background subtraction from compressive measurements (BSCM) is becoming an
active research task in video surveillance. In this paper, we propose a novel
tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames
into backgrounds with spatial-temporal correlations and foregrounds with
spatio-temporal continuity in a tensor framework. In this approach, we use 3D
total variation (TV) to enhance the spatio-temporal continuity of foregrounds,
and Tucker decomposition to model the spatio-temporal correlations of video
background. Based on this idea, we design a basic tensor RPCA model over the
video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize
the correlations among the groups of similar 3D patches of video background, we
further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint
tensor Tucker decompositions of 3D patch groups for modeling the video
background. Efficient algorithms using alternating direction method of
multipliers (ADMM) are developed to solve the proposed models. Extensive
experiments on simulated and real-world videos demonstrate the superiority of
the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for
sparse, time-varying signals using side information. Our first method utilizes
extra cross-validation measurements, and the second one exploits extra
low-resolution measurements. Unlike the majority of current CS techniques, we
do not assume that we know an upper bound on the number of significant
coefficients that comprise the images in the video sequence. Instead, we use
the side information to predict the number of significant coefficients in the
signal at the next time instant. For each image in the video sequence, our
techniques specify a fixed number of spatially-multiplexed CS measurements to
acquire, and adjust this quantity from image to image. Our strategies are
developed in the specific context of background subtraction for surveillance
video, and we experimentally validate the proposed methods on real video
sequences
Increasing Compression Ratio of Low Complexity Compressive Sensing Video Encoder with Application-Aware Configurable Mechanism
With the development of embedded video acquisition nodes and wireless video
surveillance systems, traditional video coding methods could not meet the needs
of less computing complexity any more, as well as the urgent power consumption.
So, a low-complexity compressive sensing video encoder framework with
application-aware configurable mechanism is proposed in this paper, where novel
encoding methods are exploited based on the practical purposes of the real
applications to reduce the coding complexity effectively and improve the
compression ratio (CR). Moreover, the group of processing (GOP) size and the
measurement matrix size can be configured on the encoder side according to the
post-analysis requirements of an application example of object tracking to
increase the CR of encoder as best as possible. Simulations show the proposed
framework of encoder could achieve 60X of CR when the tracking successful rate
(SR) is still keeping above 90%.Comment: 5 pages with 6figures and 1 table,conferenc
Adaptive low rank and sparse decomposition of video using compressive sensing
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.Comment: Accepted ICIP 201
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