On March 12th at 12:00, Room B-221 (ETSIT).

 

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)

 

                                                                                                                                                                                                                             
 
                                                                   
 
                                                                                                                                                             
 
      

 

 

Congestion Control for Cloud Gaming over UDP based on Round-Trip Video Latency

On March 12th at 12:00, Room B-221.

The improvement of network infrastructures in recent years, mainly due to the proliferation of FTTX (Fiber To The X) deployments, which provide high-speed and symmetric bandwidth connections, has gone hand in hand with the growth of cloud services. Among the variety of services flowing through Internet, some of them are more demanding than others in terms of the objective QoS (Quality of Service) parameters. More importantly, but also more subjectively (which makes measuring more difficult), the QoE (Quality of Experience) for some interactive audio-visual services is very dependent on the total latency perceived by the user, and on the quality of the video displayed to her.

In this talk, the CG (Cloud Gaming) paradigm was explained, due to its strict requirements on delays and losses. The challenges in terms of video streaming congestion in the downstream channel were presented, as well as a congestion control algorithm for CG platforms based on UDP. This algorithm has been designed to minimise the contribution of the downstream transmission delay to the total, end-to-end latency in the interaction-perception loop. We started by defining the RTVL (Round-Trip Video Latency), continued with an explanation of both the congestion and adaptation models, and finished by showing the experimental results.

Alberto Alós received the Telecommunication Engineering degree (integrated BSc-MSc accredited by ABET) from the Universidad de Granada, Spain, in 2014. After few years working for telecom companies he joined to UPM’s Image Processing Group (GTI) in 2017. He is involved in issues related with video coding and dynamic bitrate control.