Reconstrucción 3D de sólidos deformables mediante el uso de redes convolucionales

Este trabajo de fin de master tiene por objetivo la reconstrucción 3D de sólidos deformables mediante la utilización de redes neuronales convolucionales, en este caso con una arquitectura encoder-decoder.Dicha aplicación busca una futura aplicación en realidad aumentada.This master thesis aims to achieve 3D reconstruction of deformable solids through the use of convolutional neural networks, in this case with an encoder-decoder architecture. This work looks for a future application in augmented reality.Máster Universitario en Ingeniería Industrial (M141

Real-time human action recognition using raw depth video-based recurrent neural networks

This work proposes and compare two different approaches for real-time human action recognition (HAR) from raw depth video sequences. Both proposals are based on the convolutional long short-term memory unit, namely ConvLSTM, with differences in the architecture and the long-term learning. The former uses a video-length adaptive input data generator (stateless) whereas the latter explores the stateful ability of general recurrent neural networks but is applied in the particular case of HAR. This stateful property allows the model to accumulate discriminative patterns from previous frames without compromising computer memory. Furthermore, since the proposal uses only depth information, HAR is carried out preserving the privacy of people in the scene, since their identities can not be recognized. Both neural networks have been trained and tested using the large-scale NTU RGB+D dataset. Experimental results show that the proposed models achieve competitive recognition accuracies with lower computational cost compared with state-of-the-art methods and prove that, in the particular case of videos, the rarely-used stateful mode of recurrent neural networks significantly improves the accuracy obtained with the standard mode. The recognition accuracies obtained are 75.26% (CS) and 75.45% (CV) for the stateless model, with an average time consumption per video of 0.21 s, and 80.43% (CS) and 79.91%(CV) with 0.89 s for the stateful one.Agencia Estatal de InvestigaciónUniversidad de Alcal

Deep Shape-from-Template: Single-image quasi-isometric deformable registration and reconstruction

Shape-from-Template (SfT) solves 3D vision from a single image and a deformable 3D object model, called a template. Concretely, SfT computes registration (the correspondence between the template and the image) and reconstruction (the depth in camera frame). It constrains the object deformation to quasi-isometry. Real-time and automatic SfT represents an open problem for complex objects and imaging conditions. We present four contributions to address core unmet challenges to realise SfT with a Deep Neural Network (DNN). First, we propose a novel DNN called DeepSfT, which encodes the template in its weights and hence copes with highly complex templates. Second, we propose a semi-supervised training procedure to exploit real data. This is a practical solution to overcome the render gap that occurs when training only with simulated data. Third, we propose a geometry adaptation module to deal with different cameras at training and inference. Fourth, we combine statistical learning with physics-based reasoning. DeepSfT runs automatically and in real-time and we show with numerous experiments and an ablation study that it consistently achieves a lower 3D error than previous work. It outperforms in generalisation and achieves great performance in terms of reconstruction and registration error with wide-baseline, occlusions, illumination changes, weak texture and blur.Agencia Estatal de InvestigaciónMinisterio de Educació

Instrument validation to measure teachers' perceptions of universal design for learning

El diseño universal para el aprendizaje (dua) ha sido estudiando en los últimos años como modelo para responder a la diversidad en el aula. Sin embargo, hay pocas investigaciones que proporcionen datos sobre su eficacia. El objetivo de este artículo es validar una herramienta que permita evaluar las percepciones basadas en dua de los profesionales, a través del análisis factorial confirmatorio de una muestra de 230 profesionales. Para ello, una escala fue creada con 26 ítems: 9 ítems para el principio 1, 8 ítems para el principio 2 y 9 ítems para el principio 3. Los resultados muestran que la escala tenía una solución confirmatoria válida de tres factores, índices de confiabilidad satisfactorios y adecuada validez relacionada con los criterios. Los autores concluyen que se debe avanzar en la medición efectiva del dua en la literatura científica, que el concepto está en constante evolución y, que, por tanto, la literatura científica y los trabajos académicos deben acompañar esta corriente basada en la inclusión y el derecho de todos a tener una educación en igualdad de oportunidadesThe universal design for learning has been studied in recent years as a model for addressing diversity in the classroom. However, there are few scientific studies that provide data on their efficacy. The aim of this paper is to validate a tool which to evaluate the perceptions based on universal design for learning framework by professional, via confirmatory factor analysis, with a sample of 230 professionals. Reliability, factorial and criterial validity estimates are presented. A final scale was composite with 26 items: 9 items for principle 1, 8 items for principle 2 and 9 items for principle 3. Overall, the results shown that the scale had an adequate three-factor confirmatory solution, satisfying reliability indices, and adequate criterion-related validity. The authors conclude that we must advance in the effective measurement of udl in the scientific literature, that the concept is constantly evolving and that, therefore, scientific literature and academic work should accompany this trend based on inclusion and the right of everyone to have an equal opportunity educatio

Fast heuristic method to detect people in frontal depth images

This paper presents a new method for detecting people using only depth images captured by a camera in a frontal position. The approach is based on first detecting all the objects present in the scene and determining their average depth (distance to the camera). Next, for each object, a 3D Region of Interest (ROI) is processed around it in order to determine if the characteristics of the object correspond to the biometric characteristics of a human head. The results obtained using three public datasets captured by three depth sensors with different spatial resolutions and different operation principle (structured light, active stereo vision and Time of Flight) are presented. These results demonstrate that our method can run in realtime using a low-cost CPU platform with a high accuracy, being the processing times smaller than 1 ms per frame for a 512 × 424 image resolution with a precision of 99.26% and smaller than 4 ms per frame for a 1280 × 720 image resolution with a precision of 99.77%

People re-identification using depth and intensity information from an overhead sensor

This work presents a new people re-identification method, using depth and intensity images, both of them captured with a single static camera, located in an overhead position. The proposed solution arises from the need that exists in many areas of application to carry out identification and re-identification processes to determine, for example, the time that people remain in a certain space, while fulfilling the requirement of preserving people's privacy. This work is a novelty compared to other previous solutions, since the use of top-view images of depth and intensity allows obtaining information to perform the functions of identification and re-identification of people, maintaining their privacy and reducing occlusions. In the procedure of people identification and re-identification, only three frames of intensity and depth are used, so that the first one is obtained when the person enters the scene (frontal view), the second when it is in the central area of the scene (overhead view) and the third one when it leaves the scene (back view). In the implemented method only information from the head and shoulders of people with these three different perspectives is used. From these views three feature vectors are obtained in a simple way, two of them related to depth information and the other one related to intensity data. This increases the robustness of the method against lighting changes. The proposal has been evaluated in two different datasets and compared to other state-of-the-art proposal. The obtained results show a 96,7% success rate in re-identification, with sensors that use different operating principles, all of them obtaining depth and intensity information. Furthermore, the implemented method can work in real time on a PC, without using a GPU.Ministerio de Economía y CompetitividadAgencia Estatal de InvestigaciónUniversidad de Alcal

Exploratory Study on the Influence of Youtubers on Adolescents

El presente trabajo se centró en estudiar la relación que surge entre los adolescentes y los youtubers con el objetivo de conocer el significado y el grado de repercusión que representa seguir a un determinado youtuber. La limitada investigación científica al respecto ha señalado la importancia de aspectos como la tipología de contenidos en las emisiones de video, el estado de ánimo que pueden llegar a transmitir estos personajes en los adolescentes o el grado de cercanía e idealización que muestran los seguidores más jóvenes. Para ello, se desarrolló y validó un cuestionario ad hoc. Los resultados respaldan las dimensiones propuestas y perpetúan ciertos roles de género. Se concluye que es necesario desarrollar una educación mediática crítica, concientizando a las familias y al profesorado sobre la importancia de este tema y la necesidad de fomentar un enfoque crítico en los jóvenes respecto al “seguimiento” de estos nuevos ídolosThis paper explores the relationship between adolescents and YouTubers, with the aim of understanding the meaning and extent of the impact of following a given YouTuber. The limited scientific research on this subject has noted the importance of aspects such as the type of content in video broadcasts, the state of mind that these figures impress upon adolescents, and the level of proximity and idealization displayed by younger followers. To conduct this study, we developed and validated an ad hoc questionnaire. The results support the dimensions proposed and perpetuate certain gender roles. We conclude it is necessary to develop critical media education, raising awareness among families and teachers of the importance of this issue and the need to foster a critical approach in young people to “following” these new idol

Control y monitoreo del sistema de refrigeración de nodo de telecomuniciones /

En la actualidad, con el auge de las telecomunicaciones de uso diario en nuestras vidas, hogares y empresas los operadores prestadores de servicios de internet, datos, voz se han visto forzado a aumentar sus infraestructura y calidad de servicios para satisfacer 24 horas al día y 365 días al año las necesidades de sus clientes, para garantizar su operación con altos estándares de calidad necesitan tener en sus nodos de telecomunicaciones sistemas de refrigeración robustos y confiables los cuales deben operar 24 horas al día y 365 días al año. Un centro de procesamiento de datos (CPD) es un lugar donde se concentran todos los recursos necesarios para el procesamiento de la información de una organización. Son edificios o salas debidamente acondicionadas con una gran cantidad de equipamiento electrónico, ordenadores, redes de comunicaciones. La disponibilidad de recursos y el acceso a la información de los data center es fundamental en las operaciones diarias de las empresas, por lo tanto es imprescindible contar con un CPD estable y con la mayor disponibilidad posible. La producción de calor de los equipos que conforman un centro de datos es uno de los problemas principales y que más preocupan a sus administradores. El exceso de calor en una sala de equipos de comunicaciones afecta negativamente el rendimiento del equipo y acorta su vida útil, además de suponer un peligro en el caso de alcanzar niveles elevados. Por eso es de vital importancia el diseño de un buen sistema de refrigeración. En este diseño es fundamental el dimensionamiento del sistema, que exige comprender la cantidad de calor producida por los equipos ti junto con el que producen otras fuentes de calor que habitualmente están presentes como los SAI, la distribución de alimentación, unidades de aire acondicionado, iluminación y personas, fijarse en todo ello es básico para calcular la carga térmica. En una instalación típica las cargas que más peso tienen son: el 70% que suele corresponder a la carga de los equipos ti, el 9% a la iluminación, el 6% a la distribución de la alimentación y el 2% a las personas.Incluye bibliografía, anexo

Towards dense people detection with deep learning and depth images

This paper describes a novel DNN-based system, named PD3net, that detects multiple people from a single depth image, in real time. The proposed neural network processes a depth image and outputs a likelihood map in image coordinates, where each detection corresponds to a Gaussian-shaped local distribution, centered at each person?s head. This likelihood map encodes both the number of detected people as well as their position in the image, from which the 3D position can be computed. The proposed DNN includes spatially separated convolutions to increase performance, and runs in real-time with low budget GPUs. We use synthetic data for initially training the network, followed by fine tuning with a small amount of real data. This allows adapting the network to different scenarios without needing large and manually labeled image datasets. Due to that, the people detection system presented in this paper has numerous potential applications in different fields, such as capacity control, automatic video-surveillance, people or groups behavior analysis, healthcare or monitoring and assistance of elderly people in ambient assisted living environments. In addition, the use of depth information does not allow recognizing the identity of people in the scene, thus enabling their detection while preserving their privacy. The proposed DNN has been experimentally evaluated and compared with other state-of-the-art approaches, including both classical and DNN-based solutions, under a wide range of experimental conditions. The achieved results allows concluding that the proposed architecture and the training strategy are effective, and the network generalize to work with scenes different from those used during training. We also demonstrate that our proposal outperforms existing methods and can accurately detect people in scenes with significant occlusions.Ministerio de Economía y CompetitividadUniversidad de AlcaláAgencia Estatal de Investigació

3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

This work describes an end-to-end approach for real-time human action recognition from raw depth image-sequences. The proposal is based on a 3D fully convolutional neural network, named 3DFCNN, which automatically encodes spatio-temporal patterns from raw depth sequences. The described 3D-CNN allows actions classification from the spatial and temporal encoded information of depth sequences. The use of depth data ensures that action recognition is carried out protecting people"s privacy, since their identities can not be recognized from these data. The proposed 3DFCNN has been optimized to reach a good performance in terms of accuracy while working in real-time. Then, it has been evaluated and compared with other state-of-the-art systems in three widely used public datasets with different characteristics, demonstrating that 3DFCNN outperforms all the non-DNNbased state-of-the-art methods with a maximum accuracy of 83.6% and obtains results that are comparable to the DNN-based approaches, while maintaining a much lower computational cost of 1.09 seconds, what significantly increases its applicability in real-world environments.Agencia Estatal de InvestigaciónUniversidad de Alcal