4,458 research outputs found

    Radar and RGB-depth sensors for fall detection: a review

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    This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

    Video analysis based vehicle detection and tracking using an MCMC sampling framework

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    This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences

    Bispectrum Inversion with Application to Multireference Alignment

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    We consider the problem of estimating a signal from noisy circularly-translated versions of itself, called multireference alignment (MRA). One natural approach to MRA could be to estimate the shifts of the observations first, and infer the signal by aligning and averaging the data. In contrast, we consider a method based on estimating the signal directly, using features of the signal that are invariant under translations. Specifically, we estimate the power spectrum and the bispectrum of the signal from the observations. Under mild assumptions, these invariant features contain enough information to infer the signal. In particular, the bispectrum can be used to estimate the Fourier phases. To this end, we propose and analyze a few algorithms. Our main methods consist of non-convex optimization over the smooth manifold of phases. Empirically, in the absence of noise, these non-convex algorithms appear to converge to the target signal with random initialization. The algorithms are also robust to noise. We then suggest three additional methods. These methods are based on frequency marching, semidefinite relaxation and integer programming. The first two methods provably recover the phases exactly in the absence of noise. In the high noise level regime, the invariant features approach for MRA results in stable estimation if the number of measurements scales like the cube of the noise variance, which is the information-theoretic rate. Additionally, it requires only one pass over the data which is important at low signal-to-noise ratio when the number of observations must be large

    Adaptive recursive clustering and target tracking

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    Ship target recognition

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    Includes bibliographical references.In this report the classification of ship targets using a low resolution radar system is investigated. The thesis can be divided into two major parts. The first part summarizes research into the applications of neural networks to the low resolution non-cooperative ship target recognition problem. Three very different neural architectures are investigated and compared, namely; the Feedforward Network with Back-propagation, Kohonen's Supervised Learning Vector Quantization Network, and Simpson's Fuzzy Min-Max neural network. In all cases, pre-processing in the form of the Fourier-Modified Discrete Mellin Transform is used as a means of extracting feature vectors which are insensitive to the aspect angle of the radar. Classification tests are based on both simulated and real data. Classification accuracies of up to 93 are reported. The second part is of a purely investigative nature, and summarizes a body of research aimed at exploring new ground. The crux of this work is centered on the proposal to use synthetic range profiling in order to achieve a much higher range resolution (and hence better classification accuracies). Included in this work is a comprehensive investigation into the use of super-resolution and noise reducing eigendecomposition techniques. Algorithms investigated include the Principal Eigenvector Method, the Total Least Squares Method, and the MUSIC method. A final proposal for future research and development concerns the use of time domain averaging to improve the classification performance of the radar system. The use of an iterative correlation algorithm is investigated

    Image Simulation in Remote Sensing

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    Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers

    Toward single particle reconstruction without particle picking: Breaking the detection limit

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    Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method for biological macromolecules. In a cryo-EM experiment, the microscope produces images called micrographs. Projections of the molecule of interest are embedded in the micrographs at unknown locations, and under unknown viewing directions. Standard imaging techniques first locate these projections (detection) and then reconstruct the 3-D structure from them. Unfortunately, high noise levels hinder detection. When reliable detection is rendered impossible, the standard techniques fail. This is a problem especially for small molecules, which can be particularly hard to detect. In this paper, we propose a radically different approach: we contend that the structure could, in principle, be reconstructed directly from the micrographs, without intermediate detection. As a result, even small molecules should be within reach for cryo-EM. To support this claim, we setup a simplified mathematical model and demonstrate how our autocorrelation analysis technique allows to go directly from the micrographs to the sought signals. This involves only one pass over the micrographs, which is desirable for large experiments. We show numerical results and discuss challenges that lay ahead to turn this proof-of-concept into a competitive alternative to state-of-the-art algorithms

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
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