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Multimedia delivery in the future internet
The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio
and video are produced, distributed, shared, managed and consumed on-line through various networks,
like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white
paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked
challenges of the Networked Media in the transition to the Future of the Internet.
Internet has evolved and changed the way we work and live. End users of the Internet have been confronted
with a bewildering range of media, services and applications and of technological innovations concerning
media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace
of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more
than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so
regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected
to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising
to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged
that in a near- to mid-term future, the Internet will provide the means to share and distribute (new)
multimedia content and services with superior quality and striking flexibility, in a trusted and personalized
way, improving citizens’ quality of life, working conditions, edutainment and safety.
In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe
network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as
community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of
interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and
innovative applications “on the move”, like virtual collaboration environments, personalised services/
media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content
combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P
networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to
contribute towards such a vision.
Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6)
and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily
contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way
ahead in the area of Content Aware media delivery platforms
Learned Scalable Video Coding For Humans and Machines
Video coding has traditionally been developed to support services such as
video streaming, videoconferencing, digital TV, and so on. The main intent was
to enable human viewing of the encoded content. However, with the advances in
deep neural networks (DNNs), encoded video is increasingly being used for
automatic video analytics performed by machines. In applications such as
automatic traffic monitoring, analytics such as vehicle detection, tracking and
counting, would run continuously, while human viewing could be required
occasionally to review potential incidents. To support such applications, a new
paradigm for video coding is needed that will facilitate efficient
representation and compression of video for both machine and human use in a
scalable manner. In this manuscript, we introduce the first end-to-end
learnable video codec that supports a machine vision task in its base layer,
while its enhancement layer supports input reconstruction for human viewing.
The proposed system is constructed based on the concept of conditional coding
to achieve better compression gains. Comprehensive experimental evaluations
conducted on four standard video datasets demonstrate that our framework
outperforms both state-of-the-art learned and conventional video codecs in its
base layer, while maintaining comparable performance on the human vision task
in its enhancement layer. We will provide the implementation of the proposed
system at www.github.com upon completion of the review process.Comment: 14 pages, 16 figure
Video Surveillance Analysis as a Context for Embedded Systems and Artificial Intelligence Education
Video surveillance analysis is an exciting, active research area and an important industry application. It is a multidisciplinary field that draws on signal processing, embedded systems, and artificial intelligence topics, and is well suited to motivate student engagement in all of these areas. This paper describes the benefits of the convergence of these topics, presents a versatile video surveillance analysis process that can be used as the basis for many investigations, and presents two template exercises in tracking detected targets and in evaluating runtime efficiency. The processing chain consists of detecting changes in a scene and locating and characterizing the resulting targets. The analysis is illustrated for targets in outdoor scenes using a variety of classification features. Also, sample code for processing is included
In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks
The sixth-generation (6G) mobile networks are expected to feature the
ubiquitous deployment of machine learning and AI algorithms at the network
edge. With rapid advancements in edge AI, the time has come to realize
intelligence downloading onto edge devices (e.g., smartphones and sensors). To
materialize this version, we propose a novel technology in this article, called
in-situ model downloading, that aims to achieve transparent and real-time
replacement of on-device AI models by downloading from an AI library in the
network. Its distinctive feature is the adaptation of downloading to
time-varying situations (e.g., application, location, and time), devices'
heterogeneous storage-and-computing capacities, and channel states. A key
component of the presented framework is a set of techniques that dynamically
compress a downloaded model at the depth-level, parameter-level, or bit-level
to support adaptive model downloading. We further propose a virtualized 6G
network architecture customized for deploying in-situ model downloading with
the key feature of a three-tier (edge, local, and central) AI library.
Furthermore, experiments are conducted to quantify 6G connectivity requirements
and research opportunities pertaining to the proposed technology are discussed.Comment: The paper has been submitted to IEEE for possible publicatio
Background Subtraction in Video Surveillance
The aim of thesis is the real-time detection of moving and unconstrained surveillance environments monitored with static cameras. This is achieved based on the results provided by background subtraction. For this task, Gaussian Mixture Models (GMMs) and Kernel density estimation (KDE) are used. A thorough review of state-of-the-art formulations for the use of GMMs and KDE in the task of background subtraction reveals some further development opportunities, which are tackled in a novel GMM-based approach incorporating a variance controlling scheme. The proposed approach method is for parametric and non-parametric and gives us the better method for background subtraction, with more accuracy and easier parametrization of the models, for different environments. It also converges to more accurate models of the scenes. The detection of moving objects is achieved by using the results of background subtraction. For the detection of new static objects, two background models, learning at different rates, are used. This allows for a multi-class pixel classification, which follows the temporality of the changes detected by means of background subtraction. In a first approach, the subtraction of background models is done for parametric model and their results are shown. The second approach is for non-parametric models, where background subtraction is done using KDE non-parametric model. Furthermore, we have done some video engineering, where the background subtraction algorithm was employed so that, the background from one video and the foreground from another video are merged to form a new video. By doing this way, we can also do more complex video engineering with multiple videos. Finally, the results provided by region analysis can be used to improve the quality of the background models, therefore, considerably improving the detection results
Revisión de algoritmos, métodos y técnicas para la detección de UAVs y UAS en aplicaciones de audio, radiofrecuencia y video
Unmanned Aerial Vehicles (UAVs), also known as drones, have had an exponential evolution in recent times due in large part to the development of technologies that enhance the development of these devices. This has resulted in increasingly affordable and better-equipped artifacts, which implies their application in new fields such as agriculture, transport, monitoring, and aerial photography. However, drones have also been used in terrorist acts, privacy violations, and espionage, in addition to involuntary accidents in high-risk zones such as airports. In response to these events, multiple technologies have been introduced to control and monitor the airspace in order to ensure protection in risk areas. This paper is a review of the state of the art of the techniques, methods, and algorithms used in video, radiofrequency, and audio-based applications to detect UAVs and Unmanned Aircraft Systems (UAS). This study can serve as a starting point to develop future drone detection systems with the most convenient technologies that meet certain requirements of optimal scalability, portability, reliability, and availability.Los vehĂculos aĂ©reos no tripulados, conocidos tambiĂ©n como drones, han tenido una evoluciĂłn exponencial en los Ăşltimos tiempos, debido en gran parte al desarrollo de las tecnologĂas que potencian su desarrollo, lo cual ha desencadenado en artefactos cada vez más asequibles y con mejores prestaciones, lo que implica el desarrollo de nuevas aplicaciones como agricultura, transporte, monitoreo, fotografĂa aĂ©rea, entre otras. No obstante, los drones se han utilizado tambiĂ©n en actos terroristas, violaciones a la privacidad y espionaje, además de haber producido accidentes involuntarios en zonas de alto riesgo de operaciĂłn como aeropuertos. En respuesta a dichos eventos, aparecen tecnologĂas que permiten controlar y monitorear el espacio aĂ©reo, con el fin de garantizar la protecciĂłn en zonas de riesgo. En este artĂculo se realiza un estudio del estado del arte de la tĂ©cnicas, mĂ©todos y algoritmos basados en video, en análisis de sonido y en radio frecuencia, para tener un punto de partida que permita el desarrollo en el futuro de un sistema de detecciĂłn de drones, con las tecnologĂas más propicias, segĂşn los requerimientos que puedan ser planteados con las caracterĂsticas de escalabilidad, portabilidad, confiabilidad y disponibilidad Ăłptimas
Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization
Principal component analysis (PCA) is widely used for dimensionality
reduction, with well-documented merits in various applications involving
high-dimensional data, including computer vision, preference measurement, and
bioinformatics. In this context, the fresh look advocated here permeates
benefits from variable selection and compressive sampling, to robustify PCA
against outliers. A least-trimmed squares estimator of a low-rank bilinear
factor analysis model is shown closely related to that obtained from an
-(pseudo)norm-regularized criterion encouraging sparsity in a matrix
explicitly modeling the outliers. This connection suggests robust PCA schemes
based on convex relaxation, which lead naturally to a family of robust
estimators encompassing Huber's optimal M-class as a special case. Outliers are
identified by tuning a regularization parameter, which amounts to controlling
sparsity of the outlier matrix along the whole robustification path of (group)
least-absolute shrinkage and selection operator (Lasso) solutions. Beyond its
neat ties to robust statistics, the developed outlier-aware PCA framework is
versatile to accommodate novel and scalable algorithms to: i) track the
low-rank signal subspace robustly, as new data are acquired in real time; and
ii) determine principal components robustly in (possibly) infinite-dimensional
feature spaces. Synthetic and real data tests corroborate the effectiveness of
the proposed robust PCA schemes, when used to identify aberrant responses in
personality assessment surveys, as well as unveil communities in social
networks, and intruders from video surveillance data.Comment: 30 pages, submitted to IEEE Transactions on Signal Processin
A Review of Object Visual Detection for Intelligent Vehicles
This paper contains the details of different object detection (OD) techniques, object iden-tification's relationship with video investigation, and picture understanding, it has pulled in much exploration consideration as of late. Customary item identification strat-egies are based on high-quality highlights and shallow teachable models. This survey paper presents one such strategy which is named as Optical Flow method (OFM). This strategy is discovered to be stronger and more effective for moving item recognition and the equivalent has been appeared by an investigation in this review paper. Applying optical stream to a picture gives stream vectors of the focuses comparing to the moving items. Next piece of denoting the necessary moving object of interest checks to the post-preparing. Post handling is the real commitment of the review paper for moving item identification issues. Their presentation effectively deteriorates by developing com-plex troupes which join numerous low-level picture highlights with significant level set-ting from object indicators and scene classifiers. With the fast advancement in profound learning, all the more useful assets, which can learn semantic, significant level, further highlights, are acquainted with address the issues existing in customary designs. These models carry on contrastingly in network design, preparing system, and advancement work, and so on in this review paper, we give an audit on profound learning-based item location systems. Our survey starts with a short presentation on the historical backdrop of profound learning and its agent device, in particular, Convolutional Neural Network (CNN) and region-based convolutional neural networks (R-CNN)
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