1,721,012 research outputs found
On Motion Analysis of Multiple Time-Variant Objects in Video Sequences
No full textMotion analysis and speed estimation from video signals is a topic of increasing interest, e.g., in the field of traffic monitoring and road surveillance. Moving objects in a considered video sequence may undergo several modifications either due to perspectival issues, that depend on the camera placement, or periodic behaviours. Hence, geometrical transformations, such as scaling and rotations, and periodic features are often superimposed to the motion of an object of interest. In this paper we present a novel method to speed estimation that applies to video streams framing multiple objects which experience a dynamic change throughout the video duration. Applications and results are presented to assess the robustness of the proposed algorithm
A maximum likelihood approach to speed estimation of foreground objects in video signals
No full textMotion and speed estimation play a key role in computer vision and video processing for various application scenarios. Existing algorithms are mainly based on projected and apparent motion models and are currently used in many contexts, such as automotive security and driver assistance, industrial automation and inspection systems, video surveillance, human activity tracking and biomedical solutions, including monitoring of vital signs. In this paper, a general Maximum Likelihood (ML) approach to speed estimation of foreground objects in video streams is proposed. Application examples are presented and the performance of the proposed algorithms is discussed and compared with more conventional solutions
On the Outage Capacity of the Massive MIMO Diversity Channel
Full text linkWe consider the massive Multiple Input Multiple Output (MIMO) diversity
channel affected by independent and identically distributed Rayleigh fading,
with linear processing at both transmitter and receiver sides, and analyze the
outage capacity for large number of antennas. We first discuss the classical
Single Input Multiple Output (SIMO) diversity channel that uses Maximal Ratio
Combining (MRC) or Selection Combining (SC). For MRC, a numerical computation
and a Gaussian Approximation (GA) are considered, whereas for SC an exact
evaluation is possible. The analysis is then straightforwardly extended to the
Multiple Input Single Output (MISO) system that uses Maximal Ratio Transmission
(MRT) or transmit antenna selection. The general Multiple Input Multiple Output
(MIMO) system that pursues full diversity is finally considered, with both
optimal linear processing and simple antenna selection at both transmitter and
receiver. If the number of antennas is sufficiently large on at least one side,
the outage capacity of each considered diversity channel approaches that of a
suitable reference Additive White Gaussian Noise (AWGN) channel with properly
defined Signal-to-Noise Ratio (SNR), which provides a performance benchmark.
This conclusion is valid for large but realistic number of antennas compatible
with the assumption of independent fading
Information rate analysis of the oversampled phase-noise channel
No full textIn this paper, we analyze the information rate of a phase-noise discrete-time channel obtained by sampling the continuous-time channel. In particular, we assume that more than one sample per data symbol may be available at the receiver, so that this channel is referred to as "oversampled." This work extends previous literature work by taking into account the presence of a bandlimited shaping pulse at the transmitter with time support not limited to the symbol period, possibly causing inter-symbol interference. Our results show that for sufficiently large signal-to-noise ratio (SNR) it is possible to recover the performance degradation caused by phase noise, also for quite large values of the phase noise intensity
Video simulation of apnoea episodes
No full textThis paper presents a video simulator of apnoea episodes. A simple Continuous-Time Markov Chain (CTMC) model, describing the apnoea statistical behaviour, is combined with a properly designed video processing tool to insert apnoea episodes in the video recording of a normally breathing patient. In particular, the simulator has been applied to videos of newborns in order to simulate respiratory arrests. The obtained video streams are processed through a previously developed video processing-based system for automatic detection of apnoeas. Because of the rarity of apnoeas and the consequent limited availability of video recordings, the use of this simulator can be very helpful to test and design algorithms for detection of apnoea events. The presented results show that these events can indeed be simulated by the proposed video processing method in an accurate and reliable fashion. The proposed simulation approach can thus be very helpful for the implementation of innovative video-based, non-invasive monitoring systems or telemedical devices for in-home care
Pressure matching with optimized target phase for personal sound zone systems
No full textPressure matching (PM) is an advanced digital signal processing technique aimed at designing loudspeaker filters to achieve a target acoustic field in a desired sound region. This enables to establish personal sound zones (PSZs) in a desired environment. The target sound field is chosen during the design stage and influences the performance of the system in terms of acoustic contrast (AC) between bright and dark zones, as well as fidelity of the reproduced audio. The sound regions are represented by groups of control points (microphones) properly placed in the considered environment. With more than one control point in the bright region, the same target sound field is usually considered for all the microphones. This paper investigates the optimization of the phase of the target acoustic field required by the PM algorithm in terms of AC. The achievable performance is analyzed in a realistic automotive environment with two sound zones and two control points per zone. The numerical results show a potential improvement of the achievable AC at the cost of some spatial effects and possibly non constant group delay in the reproduced sound, which should nonetheless be tolerable in voice applications
Motion magnification algorithms for video-based breathing monitoring
No full textIn this paper, we present two video processing techniques for contact-less estimation of the Respiratory Rate (RR) of framed subjects. Due to the modest extent of movements related to respiration in both infants and adults, specific algorithms to efficiently detect breathing are needed. For this reason, motion-related variations in video signals are exploited to identify respiration of the monitored patient and simultaneously estimate the RR over time. Our estimation methods rely on two motion magnification algorithms that are exploited to enhance the subtle respiration-related movements. In particular, amplitude- and phase-based algorithms for motion magnification are considered to extract reliable motion signals. The proposed estimation systems perform both spatial decomposition of the video frames combined with proper temporal filtering to extract breathing information. After periodic (or quasi-periodic) respiratory signals are extracted and jointly analysed, we apply the Maximum Likelihood (ML) criterion to estimate the fundamental frequency, corresponding to the RR. The performance of the presented methods is first assessed by comparison with reference data. Videos framing different subjects, i.e., newborns and adults, are tested. Finally, the RR estimation accuracy of both methods is measured in terms of normalized Root Mean Squared Error (RMSE), demonstrating the superiority, performance-wise, of the phase-based method
Extraction of video features for real-time detection of neonatal seizures
No full textThis paper presents a novel approach to the extraction of video features for real-time detection of neonatal seizures. In particular, after identification of a proper Region Of Interest (ROI) within the video frame, the broadening factor and the maximum distance between consecutive pairs of zeros of a properly extracted average differential luminosity signal are shown to be relevant features for a diagnosis. The ROI is selected by defining an area around the point where the maximum amplitude of the optical flow vector of that video frame sequence is observed. The located point is then tracked by an algorithm based on template matching and optical flow. The proposed approach allows to differentiate pathological movements (e.g., clonic and myoclonic seizures) from random ones. © 2011 IEEE
Video-based solutions for newborn monitoring
No full textEfficient monitoring of vital signs is a fundamental tool in disease prevention and medical diagnostics. Main physiological parameters to monitor are not only heart rate, blood pressure, respiratory rate and body temperature, but motion analysis may also provide essential information about the clinical status of a patient. Very specific pathological movements can indeed be signs of important or potentially threatening disorders. Besides being almost exclusively performed in hospital settings, conventional monitoring often requires a contact with the body of the patient that makes traditional systems possibly invasive and uncomfortable, especially if applied on newborns. To make home care more accessible and comfortable, novel methods for remote and contactless monitoring have been developed in the recent years. Among others, appealing solutions that have received recent research attention are based on video processing techniques that allow to capture and analyze the movements of a patient in a contactless fashion
Respiratory rate monitoring by maximum likelihood video processing
No full textA novel video processing-based method for remote estimation of the respiratory rate (RR) is proposed. Relying on the fact that breathing involves quasi-periodic movements, this technique employs a generalized model of pixel-wise periodicity and applies a maximum likelihood (ML) criterion. The system first selects suitable regions of interest (ROI) mainly affected by respiratory movements. The obtained ROI are jointly analyzed for the estimation of the fundamental frequency, which is strictly related to the RR of the patient. A large motion detection algorithm is also applied, in order to exclude, from RR estimation, ROI possibly affected by unrelated large movements. The RRs estimated by the proposed system are compared with those extracted by a pneumograph and a previously proposed video processing algorithm. The results, albeit preliminary, show a good agreement with the pneumograph and a clear improvement over the previously proposed algorithm
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