Nerea Aranjuelo

Vicomtech · Intelligent Transport Systems and Engineering Department

Nowadays, containerized freight transport is one of the most important transportation systems that is undergoing an automation process due to the Deep Learning success. However, it suffers from a lack of annotated data in order to incorporate state-of-the-art neural network models to its systems. In this paper we present an innovative methodology t...

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 solut...

In this paper, we propose an approach to optimize the deployment of on-board video analytics for checking the correct positioning of luggage in aircraft cabins. The system consists of embedded cameras installed on top of the cabin and a heterogeneous embedded processor. Each camera covers multiple regions of interest (i.e., multiple seats or aisle...

Deep neural network (DNN)-based vision systems could improve passenger transportation safety by automating processes such as verifying the correct positioning of luggage, seat occupancy, etc. Abundant and well-distributed data are essential to make DNNs learn appropriate pattern recognition features and have enough generalization ability. The use o...

In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting t...

Face recognition provides a desirable solution for authentication and surveillance in Internet of Things platforms for elderly care. However, its inclusion is challenging because of the possibly reduced interaction capabilities of users, the high variety of interaction devices, and the need of managing biometric data securely. Our approach relies o...

To train Deep Neural Networks (DNNs)-based methods, suitable training data are key to help DNNs learn appropriate pattern recognition features. The use of synthetic data may help in generating sufficient and balanced data. However, models trained with such data often present a domain gap when applied to real-world scenarios. Many studies focus on t...

ABSTRACT Objective: The study aimed to characterize morphological changes of the retinal microvascular network during the progression of diabetic retinopathy. Methods: Publicly available retinal images captured by a digital fundus camera from DIARETDB1 and STARE databases were used. The retinal microvessels were segmented using the automatic metho...

Current 3D object detectors from Bird’s Eye View (BEV) LiDAR point cloud data rely on Convolutional Neural Networks (CNNs), which have originally been designed for camera images. Therefore, they look for the same target features, regardless of the position of the objects with respect to the sensor. Discarding this spatial information makes 3D objec...

This paper presents a new approach to 3D object detection that leverages the properties of the data obtained by a LiDAR sensor. State-of-the-art detectors use neural network architectures based on assumptions valid for camera images. However, point clouds obtained from LiDAR are fundamentally different. Most detectors use shared filter kernels to e...

This chapter describes an approach for improved multimedia analysis as part of an ICT-based tool for community policing. It includes technology for automatic processing of audio, image and video contents sent as evidence by the citizens to the police. In addition to technical details of their development, results of their performance within initial...

This paper introduces a web application for point cloud annotation that is used in the advanced driver assistance systems field. Apart from the point cloud viewer, the web tool has an object viewer and a timeline to define the attributes of the annotations and a video viewer to validate the point cloud annotations with the corresponding video image...

Objective: The study aimed to characterize morphological changes of the retinal microvascular network during the progression of diabetic retinopathy. Methods: Publicly available retinal images captured by a digital fundus camera from DIARETDB1 and STARE databases were used. The retinal microvessels were segmented using the automatic method and vasc...

Multimodal deep learning is about learning features over multiple modalities. Impressive progress has been made in deep learning solutions that rely on a single sensor modality for advanced driving. However, these approaches are limited to cover certain functionalities. The potential of multimodal sensor fusion has been very little exploited, altho...

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered...

In this study, the authors present a learning-free method for inferring kinematically plausible three-dimensional (3D) human body poses contextualised in a predefined 3D world, given a set of 2D body features extracted from monocular images. This contextualisation has the advantage of providing further semantic information about the observed scene....