67 research outputs found

    ML-driven provisioning and management of vertical services in automated cellular networks

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    One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.This work was supported by the EU Commission through the 5GROWTH project (Grant Agreement No. 856709), Spanish MINECO 5G-REFINE project (TEC2017-88373-R), and Generalitat de Catalunya 2017 SGR 1195.Publicad

    4DFAB: a large scale 4D facial expression database for biometric applications

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    The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases. To this end, we propose 4DFAB, a new large scale database of dynamic high-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour. In this paper, we conduct several experiments and demonstrate the usefulness of the database for various applications. The database will be made publicly available for research purposes

    Evaluación de métodos para realizar resúmenes automáticos de vídeos

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    [Resumen] En este trabajo se estudian, presentan y evalúan tres métodos que permiten realizar resúmenes de vídeos de manera automática, manteniendo la información del vídeo que cada uno de los métodos presentados considera como esencial. Se han revisado los métodos Video2GIF, basado en una red neuronal convolucional de aprendizaje profundo, Move Detector, un algoritmo que detecta y almacena los fotogramas que contienen movimiento, y Peaks Volume, que resume en función de un análisis del espectro de audio del vídeo. La evaluación de los métodos Video2GIF y Peaks Volume se ha realizado utilizando el dataset VSUMM, y la evaluación del método Move Detector, utilizando el dataset VIRAT. Los resúmenes obtenidos se han evaluado utilizando CUS (Comparison of User Summaries). A partir de los mismos se puede concluir que los resultados obtenidos con Video2GIF contienen la información más relevante del vídeo original cuando este contiene escenas cortas que albergan acciones humanas, dado que este método utiliza una red entrenada con dicho propósito, mientras que Peaks Volume ha destacado en el resumen de documentales, pero también ha conseguido unos resultados superiores a 0.4 sobre 1 en el resto de categorías de vídeos reduciendo la duración del vídeo original a la mitad o menos

    Video-based human action recognition using deep learning: a review

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    Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors. The ability to recognize, understand and predict complex human actions enables the construction of many important applications such as intelligent surveillance systems, human-computer interfaces, health care, security and military applications. In recent years, deep learning has been given particular attention by the computer vision community. This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques. We present the most important deep learning models for recognizing human actions, analyze them to provide the current progress of deep learning algorithms applied to solve human action recognition problems in realistic videos highlighting their advantages and disadvantages. Based on the quantitative analysis using recognition accuracies reported in the literature, our study identies state-of-the-art deep architectures in action recognition and then provides current trends and open problems for future works in this led.This work was supported by the Cen-tre d'Etudes et d'Expertise sur les Risques, l'environnement la mobilité et l'aménagement (CEREMA) and the UC3M Conex-Marie Curie Program.No publicad

    4DFAB: a large scale 4D facial expression database for biometric applications

    Get PDF
    The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases. To this end, we propose 4DFAB, a new large scale database of dynamic high-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour. In this paper, we conduct several experiments and demonstrate the usefulness of the database for various applications. The database will be made publicly available for research purposes

    A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

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    A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19

    Estación meteorológica inalámbrica, de muy bajo consumo e inteligencia embebida

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    El trabajo aborda la realización de una estación meteorológica alimentada mediante una placa solar. La placa emisora cuenta con una serie de sensores que dan información a cerca de la temperatura, la humedad, la presión y la iluminación. Los datos son recibidos y se implementan algoritmos de predicción del tiempo cuyo resultado es visualizado por el usuario. Uno de los objetivos fundamentales es que el sistema resultante sea de muy bajo consumo para que pueda operar durante largos períodos
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