16 research outputs found
Compressive sensing based velocity estimation in video data
This paper considers the use of compressive sensing based algorithms for
velocity estimation of moving vehicles. The procedure is based on sparse
reconstruction algorithms combined with time-frequency analysis applied to
video data. This algorithm provides an accurate estimation of object's velocity
even in the case of a very reduced number of available video frames. The
influence of crucial parameters is analysed for different types of moving
vehicles.Comment: 4 pages, 5 figure
Lensless Imaging by Compressive Sensing
In this paper, we propose a lensless compressive imaging architecture. The
architecture consists of two components, an aperture assembly and a sensor. No
lens is used. The aperture assembly consists of a two dimensional array of
aperture elements. The transmittance of each aperture element is independently
controllable. The sensor is a single detection element. A compressive sensing
matrix is implemented by adjusting the transmittance of the individual aperture
elements according to the values of the sensing matrix. The proposed
architecture is simple and reliable because no lens is used. The architecture
can be used for capturing images of visible and other spectra such as infrared,
or millimeter waves, in surveillance applications for detecting anomalies or
extracting features such as speed of moving objects. Multiple sensors may be
used with a single aperture assembly to capture multi-view images
simultaneously. A prototype was built by using a LCD panel and a photoelectric
sensor for capturing images of visible spectrum.Comment: Accepted ICIP 2013. 5 Pages, 7 Figures. arXiv admin note: substantial
text overlap with arXiv:1302.178
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
Non-recursive method for motion detection from a compressive-sampled video stream
This paper introduces a non-recursive algorithm for motion detection directly from the analysis of compressed samples. The objective of this research is to create an algorithm able to detect, in real-time, the presence of moving objects over a fixed background from a compressive-sampled greyscale video stream. Many difficulties arise using this type of algorithm because it violates the fundamental principles of compressive sensing reconstruction that lie beneath traditional recursive methods. Recursive reconstruction methods even if accurate need large amounts of time and resources because they aim to retrieve all of the information contained within a scene. Our method is based on two key considerations. The first is that the targeted information of a moving element compared to a fixed background is really small. The second is an appropriate choice of a sub-Gaussian compressive sampling strategy. Our aim is to reduce the focus of general reconstruction in order to retrieve only objects of interest. This algorithm can be used to process compressed samples derived from a video stream with a speed of 100fps. This makes possible to detect the presence of moving objects directly from compressed samples with limited resources.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-R, IPT-2011-1625-430000, IPC-20111009 CDTIJunta de Andalucía TIC 2338–2013Office of Naval Research (USA) N00014141035
Multi-view in Lensless Compressive Imaging
Multi-view images are acquired by a lensless compressive imaging
architecture, which consists of an aperture assembly and multiple sensors. The
aperture assembly consists of a two dimensional array of aperture elements
whose transmittance can be individually controlled to implement a compressive
sensing matrix. For each transmittance pattern of the aperture assembly, each
of the sensors takes a measurement. The measurement vectors from the multiple
sensors represent multi-view images of the same scene. We present theoretical
framework for multi-view reconstruction and experimental results for enhancing
quality of image using multi-view.Comment: Accepted for presentation at PCS 2013 as Paper #1021; 4 pages, 4
figures. arXiv admin note: text overlap with arXiv:1302.178
Improved l1-SPIRiT using 3D walsh transform-based sparsity basis
l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI) with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure. The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency