Symbolic representation of scenarios in Bologna airport on virtual reality concept

This paper is a part of a big Project named Retina Project, which is focused in reduce the workload of an ATCO. It uses the last technological advances as Virtual Reality concept. The work has consisted in studying the different awareness situations that happens daily in Bologna Airport. It has been analysed one scenario with good visibility where the sun predominates and two other scenarios with poor visibility where the rain and the fog dominate. Due to the study of visibility in the three scenarios computed, the conclusion obtained is that the overlay must be shown with a constant dimension regardless the position of the aircraft to be readable by the ATC and also, the frame and the flight strip should be coloured in a showy colour (like red) for a better control by the ATCO

Low-Resolution Vision for Autonomous Mobile Robots

The goal of this research is to develop algorithms using low-resolution images to perceive and understand a typical indoor environment and thereby enable a mobile robot to autonomously navigate such an environment. We present techniques for three problems: autonomous exploration, corridor classification, and minimalistic geometric representation of an indoor environment for navigation. First, we present a technique for mobile robot exploration in unknown indoor environments using only a single forward-facing camera. Rather than processing all the data, the method intermittently examines only small 32X24 downsampled grayscale images. We show that for the task of indoor exploration the visual information is highly redundant, allowing successful navigation even using only a small fraction (0.02%) of the available data. The method keeps the robot centered in the corridor by estimating two state parameters: the orientation within the corridor and the distance to the end of the corridor. The orientation is determined by combining the results of five complementary measures, while the estimated distance to the end combines the results of three complementary measures. These measures, which are predominantly information-theoretic, are analyzed independently, and the combined system is tested in several unknown corridor buildings exhibiting a wide variety of appearances, showing the sufficiency of low-resolution visual information for mobile robot exploration. Because the algorithm discards such a large percentage (99.98%) of the information both spatially and temporally, processing occurs at an average of 1000 frames per second, or equivalently takes a small fraction of the CPU. Second, we present an algorithm using image entropy to detect and classify corridor junctions from low resolution images. Because entropy can be used to perceive depth, it can be used to detect an open corridor in a set of images recorded by turning a robot at a junction by 360 degrees. Our algorithm involves detecting peaks from continuously measured entropy values and determining the angular distance between the detected peaks to determine the type of junction that was recorded (either middle, L-junction, T-junction, dead-end, or cross junction). We show that the same algorithm can be used to detect open corridors from both monocular as well as omnidirectional images. Third, we propose a minimalistic corridor representation consisting of the orientation line (center) and the wall-floor boundaries (lateral limit). The representation is extracted from low-resolution images using a novel combination of information theoretic measures and gradient cues. Our study investigates the impact of image resolution upon the accuracy of extracting such a geometry, showing that centerline and wall-floor boundaries can be estimated with reasonable accuracy even in texture-poor environments with low-resolution images. In a database of 7 unique corridor sequences for orientation measurements, less than 2% additional error was observed as the resolution of the image decreased by 99.9%

Enhancing the broadcasted TV consumption experience with broadband omnidirectional video content

[EN] The current wide range of heterogeneous consumption devices and delivery technologies, offers the opportunity to provide related contents in order to enhance and enrich the TV consumption experience. This paper describes a solution to handle the delivery and synchronous consumption of traditional broadcast TV content and related broadband omnidirectional video content. The solution is intended to support both hybrid (broadcast/broadband) delivery technologies and has been designed to be compatible with the Hybrid Broadcast Broadband TV (HbbTV) standard. In particular, some specifications of HbbTV, such as the use of global timestamps or discovery mechanisms, have been adopted. However, additional functionalities have been designed to achieve accurate synchronization and to support the playout of omnidirectional video content in current consumption devices. In order to prove that commercial hybrid environments could be immediately enhanced with this type of content, the proposed solution has been included in a testbed, and objectively and subjectively evaluated. Regarding the omnidirectional video content, the two most common types of projections are supported: equirectangular and cube map. The results of the objective assessment show that the playout of broadband delivered omnidirectional video content in companion devices can be accurately synchronized with the playout on TV of traditional broadcast 2D content. The results of the subjective assessment show the high interest of users in this type of new enriched and immersive experience that contributes to enhance their Quality of Experience (QoE) and engagement.This work was supported by the Generalitat Valenciana, Investigacion Competitiva Proyectos, through the Research and Development Program Grants for Research Groups to be Consolidated, under Grant AICO/2017/059 and Grant AICO/2017Marfil-Reguero, D.; Boronat, F.; López, J.; Vidal Meló, A. (2019). Enhancing the broadcasted TV consumption experience with broadband omnidirectional video content. IEEE Access. 7:171864-171883. https://doi.org/10.1109/ACCESS.2019.2956084S171864171883

Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation

Pre-captured immersive environments using omnidirectional cameras provide a wide range of virtual reality applications. Previous research has shown that manipulating the eye height in egocentric virtual environments can significantly affect distance perception and immersion. However, the influence of eye height in pre-captured real environments has received less attention due to the difficulty of altering the perspective after finishing the capture process. To explore this influence, we first propose a pilot study that captures real environments with multiple eye heights and asks participants to judge the egocentric distances and immersion. If a significant influence is confirmed, an effective image-based approach to adapt pre-captured real-world environments to the user's eye height would be desirable. Motivated by the study, we propose a learning-based approach for synthesizing novel views for omnidirectional images with altered eye heights. This approach employs a multitask architecture that learns depth and semantic segmentation in two formats, and generates high-quality depth and semantic segmentation to facilitate the inpainting stage. With the improved omnidirectional-aware layered depth image, our approach synthesizes natural and realistic visuals for eye height adaptation. Quantitative and qualitative evaluation shows favorable results against state-of-the-art methods, and an extensive user study verifies improved perception and immersion for pre-captured real-world environments.Comment: 10 pages, 13 figures, 3 tables, submitted to ISMAR 202