Paper accepted in IEEE Signal Processing

 

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)

 

                                                                                                                                                                                                                             
 
                                                                   
 
                                                                                                                                                             
 
      

 

 

"Subjective Assessment of Super Multiview Video with Coding Artifacts" 

Rocío Recio, Pablo Carballeira, Jesús Gutiérrez, Narciso García

 IEEE Signal Processing Letters, vol. 24, no. 6, pp. 868-871, Jun. 2017  (doi: 10.1109/LSP.2017.2695897

The subjective assessment of super multiview (SMV) video considers two main perceptual factors: image quality and visual comfort at the viewpoint transition. While previous works only covered raw content with high levels of visual comfort, this work supersedes them by targeting the subjective assessment of SMV content with coding artifacts. The outcome of this analysis yields important conclusions regarding the relationship between these two factors, indicating that 1) the perceived image quality is independent from the view point change speed, and 2) the perceived visual comfort at the view point transition is independent from the image quality. These conclusions facilitate the extension of the scope of existing subjective perception models, designed for raw SMV content, to coded content.