Listar por autor "García-González, Jorge"
Mostrando ítems 1-20 de 20
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Anomalous trajectory detection for automated traffic video surveillance
Fernández-Rodríguez, Jose David; García-González, Jorge; Benítez-Rochel, Rafaela
; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel; García-González, Jorge[et al.] (2022)
Vehicle trajectories extracted from traffic video sequences can be helpful for many purposes. In particular, the analysis of detected anomalous trajectories may enhance drivers’ safety. This work proposes a methodology to ... -
Automate d lab eling of training data for improved object detection in traffic videos by fine-tuned deep convolutional neural networks
García Aguilar, Iván; García-González, Jorge; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (Elsevier, 2023)
The exponential increase in the use of technology in road management systems has led to real-time vi- sual information in thousands of locations on road networks. A previous step in preventing or detecting accidents involves ... -
Automated detection of vehicles with anomalous trajectories in traffic surveillance videos.
Fernández-Rodríguez, Jose David; García-González, Jorge; Benítez-Rochel, Rafaela; Molina-Cabello, Miguel Ángel; Ramos-Jiménez, Gonzalo Pascual; López-Rubio, Ezequiel[et al.] (IOS Press, 2023-05-10)
Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this ... -
Background modeling by shifted tilings of stacked denoising autoencoders
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (2019-06-18)
The effective processing of visual data without interruption is currently of supreme importance. For that purpose, the analysis system must adapt to events that may affect the data quality and maintain its performance level ... -
Background modeling for video sequences by stacked denoising autoencoders
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel (2018-11-05)
Nowadays, the analysis and extraction of relevant information in visual data flows is of paramount importance. These images sequences can last for hours, which implies that the model must adapt to all kinds of circumstances ... -
Clasificación de imágenes de lesiones cutáneas mediante redes neuronales convolucionales y aprendizaje profundo
(2020-11-24)El cáncer de piel es el cuarto tipo de cáncer más común en el mundo, el primero en numerosos países y, además, se espera que su incidencia aumente en las próximas décadas. Gran parte de ellos presentan una mortalidad ... -
Clasificación de noticias mediante técnicas de procesamiento del lenguaje natural basadas en aprendizaje profundo.
(2021-06)Uno de los sectores más afectados por la evolución tecnológica de los últimos años es el periodístico, debido tanto al auge del periodismo digital como a la personalización de contenidos mediante el estudio de perfiles de ... -
Deep Learning Neural Networks to Detect Anomalies in Video Sequences.
(UMA Editorial, 2024)El principal problema abordado en esta tesis doctoral es la detección de anomalías de primer plano en secuencias de video genéricas mediante el uso de técnicas de Aprendizaje Profundo especialmente enfocadas a ser robustas ... -
Design of an artificial language for Human-Computer interaction.
(2017-03-29)This project has as objective to note motivations to search and develop an optimal general purpose artificial language with effective and not ambiguous human-machine communication objective in long term. Following this ... -
Detection of dangerously approaching vehicles over onboard cameras by speed estimation from apparent size
García Aguilar, Iván; García-González, Jorge; Medina, Daniel; Luque-Baena, Rafael Marcos; Domínguez-Merino, Enrique; López-Rubio, Ezequiel[et al.] (Elsevier, 2023-11-17)
Autonomous driving requires information such as the velocity of other vehicles to prevent potential hazards. This work proposes a real-time deep learning-based framework to estimate vehicle speeds from image captures through ... -
Encoding generative adversarial networks for defense against image classification attacks
Rodríguez Rodríguez, José Antonio; Pérez Bravo, José María; García-González, Jorge; Molina-Cabello, Miguel Ángel; Thurnhofer-Hemsi, Karl; López-Rubio, Ezequiel[et al.] (2022)
Image classification has undergone a revolution in recent years due to the high performance of new deep learning models. However, severe security issues may impact the performance of these systems. In particular, adversarial ... -
Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel (2019-06-07)
A robust foreground detection system is presented, which is resilient to noise in video sequences. The proposed model divides each video frame in patches that are fed to a stacked denoising autoencoder, which is responsible ... -
Identificación de alimentos en comedores universitarios mediante técnicas de visión artificial y aprendizaje profundo
(2021-07)El desarrollo de una plataforma que consiga disminuir los tiempos de espera proporcionaría una mayor fluidez a las colas de los comedores y aumentaría la eficiencia en las horas punta cuando hay más afluencia de personas ... -
Moving object detection in noisy video sequences using deep convolutional disentangled representations.
García-González, Jorge; Luque-Baena, Rafael Marcos; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel (2022)
Noise robustness is crucial when approaching a moving de- tection problem since image noise is easily mistaken for movement. In order to deal with the noise, deep denoising autoencoders are commonly proposed to be applied ... -
Optimización de hiperparámetros en el modelo de reconstrucción de campos de radiancia DirectVoxGo
(2024)Neural Rendering es un método, basado en redes neuronales y otras técnicas, capaz de crear imágenes y vídeos nuevos basados en escenas preexistentes. El modelo DirectVoxGo es un software de Neural Rendering para ... -
Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks
García-González, Jorge; Molina-Cabello, Miguel Ángel; Luque-Baena, Rafael Marcos; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel (ELSEVIER, 2021-12)
Air quality and reduction of emissions in the transport sector are determinant factors in achieving a sustainable global climate. The monitoring of emissions in traffic routes can help to improve route planning and to ... -
Seguimiento de vehículos a través de videovigilancia y estimación de su velocidad
(2021-06)La aplicación de métodos de aprendizaje profundo relacionados con la visión por computador ha dado lugar a importantes mejoras en muchas aplicaciones industriales. Algunas de las aplicaciones relacionadas pueden ser la ... -
The effect of downsampling-upsampling strategy on foreground detection algorithms
Molina-Cabello, Miguel Ángel; García-González, Jorge; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (Springer Nature, 2020)
In video surveillance systems which incorporate stationary cameras, the first phase of movement object detection is crucial for the correct modelling of the behavior of these objects, as well as being the most complex in ... -
Vehicle overtaking hazard detection over onboard cameras using deep convolutional networks
García-González, Jorge; García Aguilar, Iván; Medina, Daniel; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel; Domínguez, Enrique[et al.] (2022)
The development of artificial vision systems to support driving has been of great interest in recent years, especially after new learning models based on deep learning. In this work, a framework is proposed for detecting ...