Details
Name
Vitor Manuel FilipeCluster
Computer ScienceRole
Research CoordinatorSince
01st October 2012
Nationality
PortugalCentre
Information Systems and Computer GraphicsContacts
+351222094199
vitor.m.filipe@inesctec.pt
2020
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
Authors
Alves, JP; Fonseca Ferreira, NMF; Valente, A; Soares, S; Filipe, V;
Publication
Robotics in Education - Advances in Intelligent Systems and Computing
Abstract
2020
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
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
Authors
Crisostomo, L; Fonseca Ferreira, NMF; Filipe, V;
Publication
International Journal of Advanced Robotic Systems
Abstract
Supervised Thesis
2019
Author
Salik Ram Khanal
Institution
UTAD
2018
Author
Leonel Agostinho Costa Crisóstomo
Institution
UTAD
2018
Author
Jorge Manuel Pereira Duque
Institution
UTAD
2018
Author
Salik Ram Khanal
Institution
UTAD
2017
Author
Leonel Agostinho Costa Crisóstomo
Institution
UTAD
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