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Details

  • Name

    Vitor Manuel Filipe
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st October 2012
002
Publications

2020

Correction to: A review of assistive spatial orientation and navigation technologies for the visually impaired

Authors
Fernandes, H; Costa, P; Filipe, V; Paredes, H; Barroso, J;

Publication
Universal Access in the Information Society

Abstract
The fourth author name was missed in the original publication. The correct list of authors should read as “Hugo Fernandes, Paulo Costa, Vitor Filipe, Hugo Paredes, João Barroso”. It has been corrected in this erratum. The original article has been updated. © 2017 Springer-Verlag GmbH Germany

2020

Autonomous Driving Car Competition

Authors
Alves, JP; Fonseca Ferreira, NMF; Valente, A; Soares, S; Filipe, V;

Publication
Robotics in Education - Advances in Intelligent Systems and Computing

Abstract

2020

UAV Landing Using Computer Vision Techniques for Human Detection

Authors
Safadinho, D; Ramos, J; Ribeiro, R; Filipe, V; Barroso, J; Pereira, A;

Publication
SENSORS

Abstract
The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed-without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5-10 m, with recalls from 59%-76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.

2020

Vineyard trunk detection using deep learning – An experimental device benchmark

Authors
Pinto de Aguiar, ASP; Neves dos Santos, FBN; Feliz dos Santos, LCF; de Jesus Filipe, VMD; Miranda de Sousa, AJM;

Publication
Computers and Electronics in Agriculture

Abstract

2020

Robotics services at home support

Authors
Crisostomo, L; Fonseca Ferreira, NMF; Filipe, V;

Publication
International Journal of Advanced Robotic Systems

Abstract
This article proposes a robotic system that aims to support the elderly, to comply with the medication regimen to which they are subject. The robot uses its locomotion system to move to the elderly and through computer vision detects the packaging of the medicine and identifies the person who should take it at the correct time. For the accomplishment of the task, an application was developed supported by a database with information about the elderly, the medicines that they have prescribed and the respective timetable of taking. The experimental work was done with the robot NAO, using development tools like MySQL, Python, and OpenCV. The elderly facial identification and the detection of medicine packing are performed through computer vision algorithms that process the images acquired by the robot’s camera. Experiments were carried out to evaluate the performance of object recognition, facial detection, and facial recognition algorithms, using public databases. The tests made it possible to obtain qualitative metrics about the algorithms’ performance. A proof of concept experiment was conducted in a simple scenario that recreates the environment of a dwelling with seniors who are assisted by the robot in the taking of medicines.

Supervised
thesis

2019

Facial image processing to monitor physical exercise intensity

Author
Salik Ram Khanal

Institution
UTAD

2018

Facial image processing to monitor physical exercise intensity

Author
Salik Ram Khanal

Institution
UTAD

2018

Interação Visual para a assistência a idosos

Author
Leonel Agostinho Costa Crisóstomo

Institution
UTAD

2018

MODELO DE FATORES INFLUENCIADORES DA ADOÇÃO DE CRM EM MUNICÍPIOS

Author
Jorge Manuel Pereira Duque

Institution
UTAD

2017

Sistema de monitorização do estado emocional em idosos

Author
Bruno André Soares Morais

Institution
UTAD