JupyterHub en Kubernetes

 

Research  

 

GTI Data   

 

Open databases created and software developed by the GTI and supplemental material to papers.  

 

Databases  

SportCLIP (2025): Multi-sport dataset for text-guided video summarization.
Ficosa (2024): The FNTVD dataset has been generated using the Ficosa's recording car.
MATDAT (2023):  More than 90K labeled images of martial arts tricking.
SEAW – DATASET (2022): 3 stereoscopic contents in 4K resolution at 30 fps.
UPM-GTI-Face dataset (2022): 11 different subjects captured in 4K, under 2 scenarios, and 2 face mask conditions.
LaSoDa (2022): 60 annotated images from soccer matches in five stadiums with different characteristics and light conditions.
PIROPO Database (2021):People in Indoor ROoms with Perspective and Omnidirectional cameras.
EVENT-CLASS (2021): High-quality 360-degree videos in the context of tele-education.
Parking Lot Occupancy Database (2020)
Nighttime Vehicle Detection database (NVD) (2019)
Hand gesture dataset (2019): Multi-modal Leap Motion dataset for Hand Gesture Recognition.
ViCoCoS-3D (2016): VideoConference Common Scenes in 3D.
LASIESTA database (2016): More than 20 sequences to test moving object detection and tracking algorithms.
Hand gesture database (2015): Hand-gesture database composed by high-resolution color images acquired with the Senz3D sensor.
HRRFaceD database (2014):Face database composed by high resolution images acquired with Microsoft Kinect 2 (second generation).
Lab database (2012): Set of 6 sequences to test moving object detection strategies.
Vehicle image database (2012): More than 7000 images of vehicles and roads.           

 

Software  

Empowering Computer Vision in Higher Education(2024): A Novel Tool for Enhancing Video Coding Comprehension.
Engaging students in audiovisual coding through interactive MATLAB GUIs (2024)
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem (2023)
Solving Routing Problems for Multiple Cooperative Unmanned Aerial Vehicles using Transformer Networks (2023)
Vision Transformers and Traditional Convolutional Neural Networks for Face Recognition Tasks (2023)
Faster GSAC-DNN (2023): A Deep Learning Approach to Nighttime Vehicle Detection Using a Fast Grid of Spatial Aware Classifiers.
SETForSeQ (2020): Subjective Evaluation Tool for Foreground Segmentation Quality. 
SMV Player for Oculus Rift (2016)
Bag-D3P (2016): Face recognition using depth information. 
TSLAB (2015): Tool for Semiautomatic LABeling.    

   

Supplementary material  

Soccer line mark segmentation and classification with stochastic watershed transform (2022)
A fully automatic method for segmentation of soccer playing fields (2022)
Grass band detection in soccer images for improved image registration (2022)
Evaluating the Influence of the HMD, Usability, and Fatigue in 360VR Video Quality Assessments (2020)
Automatic soccer field of play registration (2020)   
Augmented reality tool for the situational awareness improvement of UAV operators (2017)
Detection of static moving objects using multiple nonparametric background-foreground models on a Finite State Machine (2015)
Real-time nonparametric background subtraction with tracking-based foreground update (2015)  
Camera localization using trajectories and maps (2014)

 

                                                                                                                                                                                                                             
 
                                                                   
 
                                                                                                                                                             
 
      

 

 

JupyterHub en Kubernetes

On Thursday, March 13, Marcos Rodrigo and Daniel Fuertes, researchers from the rupo de Tratamiento de Imágenes (GTI), presented a new cloud computing service for the Department of Signals, Systems, and Radiocommunications (SSR) at the School of Telecommunications Engineering (ETSIT). The presentation, titled "New Cloud Computing Service (GPUs) of SSR: Presentation and User Guide," took place in Building A, SSR Laboratory, A201L, Room C, at 11:00 AM.

A New Service for the Academic Community

The main objective of this presentation was to introduce the new cloud computing service of SSR, designed to facilitate work with Deep Learning applications. This service will allow department members to use a system based on open-source solutions, similar to Google Colab, but hosted in the SSR teaching laboratory.

Presentation Content

Under the subtitle "JupyterHub on Kubernetes: Practical Guide for University Researchers in Cloud Computing Environments," Marcos and Daniel provided a detailed guide on how to use this new platform. During the presentation, the following key points were covered:

        • Basic Access: Secure login/logout, password change, and session management.
        • Practical Configuration: Creating virtual environments for projects, running code, and managing files.
        • Efficient Resource Usage: How to utilize and monitor GPU resources (CUDA), storage management, among others.

Benefits and Applications

This new service is specifically aimed at research teams that require a stable, scalable platform with no time restrictions for the development of projects in artificial intelligence, data analysis, and intensive computing.

With this initiative, SSR strengthens its commitment to innovation and technological development, providing advanced tools for the ETSIT academic community.

JupyterHubv2