Narciso Garcı́a

Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar et al.

Route planning for a fleet of vehicles is an important task in applications such as package delivery, surveillance, or transportation, often integrated within larger Intelligent Transportation Systems (ITS). This problem is commonly formulated as a Vehicle Routing Problem (VRP) known as the Team Orienteering Problem (TOP). Existing solvers for this problem primarily rely on either linear programming, which provides accurate solutions but requires computation times that grow with the size of the problem, or heuristic methods, which typically find suboptimal solutions in a shorter time. In this paper, we introduce TOP-Former, a multi-agent route planning neural network designed to efficiently and accurately solve the Team Orienteering Problem. The proposed algorithm is based on a centralized Transformer neural network capable of learning to encode the scenario (modeled as a graph) and analyze the complete context of all agents to deliver fast, precise, and collaborative solutions. Unlike other neural network-based approaches that adopt a more local perspective, TOP-Former is trained to understand the global situation of the vehicle fleet and generate solutions that maximize long-term expected returns. Extensive experiments demonstrate that the presented system outperforms most state-of-the-art methods in terms of both accuracy and computation speed.

Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar et al.

Generating trajectories for synthetic aperture radar (SAR)-equipped aircraft poses significant challenges due to terrain constraints, and the need for straight-flight segments to ensure high-quality imaging. Related works usually focus on trajectory optimization for predefined straight-flight segments that do not adapt to the target visibility, which depends on the 3D terrain and aircraft orientation. In addition, this assumption does not scale well for the multi-target problem, where multiple straight-flight segments that maximize target visibility must be defined for real-time operations. For this purpose, this paper presents a multi-stage planning system. First, the waypoint sequencing to visit all the targets is estimated. Second, straight-flight segments maximizing target visibility according to the 3D terrain are predicted using a novel neural network trained with deep reinforcement learning. Finally, the segments are connected to create a trajectory via optimization that imposes 3D Dubins curves. Evaluations demonstrate the robustness of the system for SAR missions since it ensures high-quality multi-target SAR image acquisition aware of 3D terrain and target visibility, and real-time performance.

Daniel Berjón, Carlos Cuevas, Narciso García

Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The first step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the playing field. Most existing proposals for line detection rely on edge detection and Hough transform, but radial distortion and extraneous edges cause inaccurate or spurious detections of line markings. We propose a novel strategy to automatically and accurately segment and classify line markings. First, line points are segmented thanks to a stochastic watershed transform that is robust to radial distortions, since it makes no assumptions about line straightness, and is unaffected by the presence of players or the ball. The line points are then linked to primitive structures (straight lines and ellipses) thanks to a very efficient procedure that makes no assumptions about the number of primitives that appear in each image. The strategy has been tested on a new and public database composed by 60 annotated images from matches in five stadiums. The results obtained have proven that the proposed strategy is more robust and accurate than existing approaches, achieving successful line mark detection even in challenging conditions.

Pablo Perez, Lucjan Janowski, Narciso Garcia et al.

Recent studies have shown that it is possible to characterize subject bias and variance in subjective assessment tests. Apparent differences among subjects can, for the most part, be explained by random factors. Building on that theory, we propose a subjective test design where three to four team members each rate the stimuli multiple times. The results are comparable to a high performing objective metric. This provides a quick and simple way to analyze new technologies and perform pre-tests for subjective assessment.

Marta Orduna, Pablo Pérez, Jesús Gutiérrez et al.

This paper analyzes the joint assessment of quality, spatial and social presence, empathy, attitude, and attention in three conditions: (A)visualizing and rating the quality of contents in a Head-Mounted Display (HMD), (B)visualizing the contents in an HMD,and (C)visualizing the contents in an HMD where participants can see their hands and take notes. The experiment simulates an immersive communication where participants attend conversations of different genres and from different acquisition perspectives in the context of international experiences. Video quality is evaluated with Single-Stimulus Discrete Quality Evaluation (SSDQE) methodology. Spatial and social presence are evaluated with questionnaires adapted from the literature. Initial empathy is assessed with Interpersonal Reactivity Index(IRI) and a questionnaire is designed to evaluate attitude. Attention is evaluated with 3 questions that had pass/fail answers. 54 participants were evenly distributed among A, B, and C conditions taking into account their international experience backgrounds, obtaining a diverse sample of participants. The results from the subjective test validate the proposed methodology in VR communications, showing that video quality experiments can be adapted to conditions imposed by experiments focused on the evaluation of socioemotional features in terms of contents of long-duration, actor and observer acquisition perspectives, and genre. In addition, the positive results related to the sense of presence imply that technology can be relevant in the analyzed use case. The acquisition perspective greatly influences social presence and all the contents have a positive impact on all participants on their attitude towards international experiences. The annotated dataset, Student Experiences Around the World dataset (SEAW-dataset), obtained from the experiment is made publicly available.

Pablo Carballeira, Carlos Carmona, César Díaz et al.

FVV Live is a novel end-to-end free-viewpoint video system, designed for low cost and real-time operation, based on off-the-shelf components. The system has been designed to yield high-quality free-viewpoint video using consumer-grade cameras and hardware, which enables low deployment costs and easy installation for immersive event-broadcasting or videoconferencing. The paper describes the architecture of the system, including acquisition and encoding of multiview plus depth data in several capture servers and virtual view synthesis on an edge server. All the blocks of the system have been designed to overcome the limitations imposed by hardware and network, which impact directly on the accuracy of depth data and thus on the quality of virtual view synthesis. The design of FVV Live allows for an arbitrary number of cameras and capture servers, and the results presented in this paper correspond to an implementation with nine stereo-based depth cameras. FVV Live presents low motion-to-photon and end-to-end delays, which enables seamless free-viewpoint navigation and bilateral immersive communications. Moreover, the visual quality of FVV Live has been assessed through subjective assessment with satisfactory results, and additional comparative tests show that it is preferred over state-of-the-art DIBR alternatives.

Lara Muñoz, César Díaz, Marta Orduna et al.

To properly evaluate the performance of 360VR-specific encoding and transmission schemes, and particularly of the solutions based on viewport adaptation, it is necessary to consider not only the bandwidth saved, but also the quality of the portion of the scene actually seen by users over time. With this motivation, we propose a robust, yet flexible methodology for accurately assessing the quality within the viewport along the visualization session. This procedure is based on a complete analysis of the geometric relations involved. Moreover, the designed methodology allows for both offline and online usage thanks to the use of different approximations. In this way, our methodology can be used regardless of the approach to properly evaluate the implemented strategy, obtaining a fairer comparison between them.