Conducting extensive recording campaigns to asses the safety of newly developed Automated Driving Systems (ADS) or perception algorithms has been proved to be a costly and time consuming process. This is one of the reasons why the automotive industry is adopting the scenario-based testing methodology, to verify and validate the safety of the developed ADS in their expected operating domain. The exterior perception system is the first component in the sense-plan-act process of Connected Cooperative and Automated Vehicles (CCAVs). In this context, high-fidelity simulation engines are used to replicate sensor setups at reduced cost and higher scalability than driving and capturing data from real sensors. The use of logical automotive scenario descriptions allows defining certain parameter ranges, contexts and actions to execute simulations that fulfill the desired conditions. This work proposes a methodology for generating synthetic labelled datasets to test and validate automotive perception systems using logical scenario files. Decoupling the desired sensor setup from the simulation allows reproduction and testing the same situation under different sensor setups and conditions. We implement the methodology to validate three 3D LiDAR-based object detectors in three different sensor setups. The generated sample dataset will be made public here11https://github.com/Vicomtech/Synthetic-OpenLABEL-Scenarios.

Abstract Annotated datasets have become crucial for training Machine Learning (ML) models for developing Autonomous Vehicles (AVs) and their functions. Generating these datasets usually involves a complex coordination of automation and manual effort. Moreover, most available labelling tools focus on specific media types (e.g., images or video). Consequently, they cannot perform complex labelling tasks for multi-sensor setups. Recently, ASAM published OpenLABEL, a standard designed to specify an annotation format flexible enough to support the development of automated driving features and to guarantee interoperability among different systems and providers. In this work, we present WebLabel, the first multipurpose web application tool for labelling complex multi-sensor data that is fully compliant with OpenLABEL 1.0. The proposed work analyses several labelling use cases demonstrating the standard's benefits and the application's flexibility to cover various heterogeneous requirements: image labelling, multi-view video object annotation, point-cloud view-based labelling for 3D geometries and action recognition.

The current sanitary emergency situation caused by COVID-19 has increased the interest in controlling the flow of people in indoor infrastructures, to ensure compliance with the established security measures. Top view camera-based solutions have proven to be an effective and non-invasive approach to accomplish this task. Nevertheless, current solutions suffer from scalability problems: they cover limited range areas to avoid dealing with occlusions and only work with single camera scenarios. To overcome these problems, we present an efficient and scalable people flow monitoring system that relies on three main pillars: an optimized top view human detection neural network based on YOLO-V4, capable of working with data from cameras at different heights; a multi-camera 3D detection projection and fusion procedure, which uses the camera calibration parameters for an accurate real-world positioning; and a tracking algorithm which jointly processes the 3D detections coming from all the cameras, allowing the traceability of individuals across the entire infrastructure. The conducted experiments show that the proposed system generates robust performance indicators and that it is suitable for real-time applications to control sanitary measures in large infrastructures. Furthermore, the proposed projection approach achieves an average positioning error below 0.2 meters, with an improvement of more than 4 times compared to other methods.

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

This paper presents a novel approach for iris dissimilarity computation based on Machine Learning paradigms and Computer Vision transformations. Based on the training dataset given by the MICHE II Challenge organizers, a set of classifiers has been constructed and tested, aiming at classifying a single image. The main novelty of this paper remains in the used approach to iris dissimilarity computation: given two iris images, both of them are classified using the same paradigm, obtaining the a posteriori probability for each of the considered class values. Hence, two distributions are obtained, one for each iris image, and the dissimilarity is computed as the distance between these two distributions. Experimental results indicate the appropriateness of this new approach, even though more research and experiments are needed to obtain some improvements and to accelerate the classification process.

This paper summarizes the proposal submitted by the joint team conformed by researchers from UPV and ULPGC to the Mobile Iris CHallenge Evaluation II. The approach makes use of a state-of-the-art iris segmentation technique, to later extract features making use of local descriptors. Those suitable to the problem are selected after evaluating a collection of 15 local descriptors, covering a range of different grid configuration setups. A Machine Learning approach is used, learning a supervised classifier to deal with the descriptors data. A classifier is obtained for each descriptor, and the best ones are combined in a multi-classifier system. The final step fuses the classifier outputs obtained for 5 different local descriptors, to compute the dissimilarity measure for a pair of iris images.

The Petabyte-scale data volumes in Earth Observation (EO) archives are not efficiently manageable with serial processes running on large isolated servers. Distributed storage and processing based on `big data' cloud computing frameworks needs to be considered as a part of the solution.

There is a growing gap between the multimedia production and the context centric multimedia services related to the under-exploitation of the content creation design. This could support dynamic content generation adapted to the user or display profile. Our work faces it by implementing a web platform for automatic generation of multimedia presentations based on SMIL (Synchronized Multimedia Integration Language) standard. The system is able to produce rich media with dynamic multimedia content retrieved automatically from different content databases matching the semantic context. For this purpose, we extend the standard interpretation of SMIL tags in order to accomplish a semantic translation of multimedia objects in database queries. This permits services to take benefit of production process to create customized content enhanced with real time information fed from databases. The described system has been successfully deployed to create advanced context centric weather forecasts.