IEEE Award to GTI members

 

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 Award to GTI members

José L. Herrera, Carlos R. del Blanco, and Narciso García have been named winners of the 2019 IEEE Consumer Electronics Society Chester Sall Award for the first place best paper in the IEEE Transactions on Consumer Electronics 2017.

Congratulations!

The presentation of the award will be made at the Awards Luncheon for the 2019 ICCE Conference, which will take place in Las vegas on January 11-13, 2019.

First Place Transactions Award (the original letter here)

“A Novel 2D to 3D Video Conversion System Based on a Machine Learning Approach”, IEEE Transactions on Consumer Electronics, vol. 62, no. 4, pp 429-436, November 2016. (doi: 10.1109/TCE.2016.7838096).

Abstract: TThere has been recently a significant increase in the number of available 3D displays and players. Nevertheless, the amount of 3D content has not increased in the same magnitude, creating a gap between 3D offer and demand. To reduce this difference, many algorithms have appeared that perform 2D-to-3D image and video conversion. These algorithms usually require several images from the same scene to perform the conversion. In this paper, an automatic algorithm for estimating the 3D structure of a scene from a single color image is proposed. It is based on the key assumption that color images with similar structure will likely present similar depth structures. The conversion algorithm is split into an offline and an online module to be easily deployable into consumer devices, such as smartphones or TVs. The offline module pre-processes a color+depth image database to speed up the subsequent depth estimation. The online module infers a depth prior from a color query image using the previous database as training data. Then, it is refined through a segmentation-guided filtering. The conversion algorithm has been evaluated in three publicly available databases, and compared with several state-of-theart algorithms to prove its efficiency.

You can download it here.