IEEE VR 2020

 

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)

 

                                                                                                                                                                                                                             
 
                                                                   
 
                                                                                                                                                             
 
      

 

 

IEEE VR 2020

Out of an abundance of caution surrounding COVID-19, the decision has been made to convert the in-person component of VR 2020 into an all-digital conference experience. Thus, on March 22th-26th 2020, the conference VR 2020 was an online event.

Marta Capture

Marta Orduna, a PhD student of the GTI (Grupo de Tratamiento de Imágenes), was selected to participate in the Doctoral Consortium, where she presented her work entitled “Quality, Presence, and Emotions in Virtual Reality Communications”. In addition, Marta also attended the main conference, where she presented the paper poster “Evaluating the Influence of the HMD, Usability, and Fatigue in 360VR Video Quality Assessments”. 

posterDC IEEEVR2020 vertical POSTER new 

Abstract: This doctoral thesis looks for the identification and evaluation of the factors that allow to improve the QoE of a remote client in telepresence and virtual reality scenarios. Specifically, quality and socioemotional concepts such as social and spatial presence, empathy, and emotions of being in a completely different place, as well as communicate and interact with people who are in that place. The main goals of my research are the analysis of the methodologies to evaluate video quality and socioemotional concepts, the implementation of additional tools using ML techniques to improve the QoE, and finally, experiments in real use cases.

posterDC IEEEVR2020 vertical POSTER new

Abstract: VR communications present great challenges that should be addressed in subjective assessment methodologies. Thus, the HMD, the usability of controllers and touchpad, or even the fatigue produced by the test session are factors that highly influence the QoE. In addition, in a 360VR scenario, most of the experiments are focused on the evaluation of video quality or the evaluation of socioemotional concepts such as the sense of presence. However, no standardized methodology to evaluate both kinds of concepts is available nowadays. In this paper, we present an experiment where video quality and sense of presence are jointly assessed in two of the most popular HMDs. As a result, we have observed that while presence is affected by the evaluation mechanism or duration of the test, quality is mainly affected by the HMD. This statement implies that methodologies that take into account technical concepts, such as encoding and transmission, and socioemotional concepts are necessary to obtain reliable and robust results of QoE in a VR environment. Video

Marta Orduna received the Bachelor of Engineering in Telecommunication Technologies and Services (intensification in Sound and Image) in 2016 and the Master in Telecommunication Engineering (accredited by ABET) in 2018, both from the Universidad Politécnica de Madrid (UPM), Madrid, Spain. She has been a member of the Grupo de Tratamiento de Imágenes (Image Processing Group) at the UPM since 2016. Her current research is in the area of quality, presence, and emotions in virtual reality communications. She has been involved in several projects in collaboration with Alcatel-Lucent and Nokia Bell-Labs.