Campus del Baix Llobregat
 
Projectes oferts

Projectes matriculats

Tribunals i dates de lectura

Projectes llegits
(2019-2)

DG ENG AERO/SIS TEL

DG ENG AERO/TELEMÀT

DG ENG SISTE/TELEMÀT

ENG TELEC 2NCICLE 01

ET AERO/ETT SIST 05

ET AERONÀUTICA 03

ETT SIST/ ET AERO 05

ETT SIST TELEC 00

ETT SIST TELEC 91

ETT TELEMÀTICA 00

GR ENG AERONAVEGACIÓ

GR ENG AEROPORTS

GR ENG SIS TELECOMUN

GR ENG SIST AEROESP

GR ENG TELEMÀTICA

MU AEROSPACE S&T 09

MU DRONS

MU MASTEAM 2009

MU MASTEAM 2015

Cerca projectes

Calendari TFG de dipòsit i lectura

Documentació

Web UPC


 

Projecte llegit

Títol: Machine learning on deep neural networks and object tracking applied to motion of airplanes

Estudiants que han llegit aquest projecte:

Director: MASSIGNAN, Pietro Alberto

Departament: FIS

Títol: Machine learning on deep neural networks and object tracking applied to motion of airplanes

Data inici oferta: 23-01-2020     Data finalització oferta: 23-09-2020


Estudis d'assignació del projecte:
    GR ENG AERONAVEGACIÓ
    GR ENG SIST AEROESP
Tipus: Individual
 
Lloc de realització: EETAC
 
Paraules clau:
Object detection, Object tracking, Machine learning
 
Descripció del contingut i pla d'activitats:
The objective of the proposed TFG is to develop efficient
algorithms capable of: i) identifying (multiple) objects in static
images, and ii) tracking their motion across various frames of a
video.

The student will start by solving a simple machine learning task
(e.g., recognizing hand-written digits), and then proceed to study
the more complex case of object recognition in real photos.
Finally,
he/she will analyze videos containing moving objects (like for
example multiple airplanes taxiing around an airport, or acrobatic
airplanes performing stunts in the sky), and write an algorithm
capable of following their motion.

Object recognition and tracking have immediate, fast-growing
applications in engineering, telecommunications, informatics, and
physics. The director of the proposed TFG is also a guest scientist
at the Institute of Photonic Sciences (ICFO), so that if the
occasion
arises the student will have the chance to visit the labs and the
research teams working therein, and attend relevant research
seminars.
 
Overview (resum en anglès):

The aim of this project is to understand the concepts underlying machine learning and how to implement those. To achieve this purpose, an exhaustive study of the origins of this technology has been made, describing the most popular types of neural networks, their history, and the architectures and subsequent implementations.

Three implementations of neural networks are presented, using world-known datasets. In the last implementation, an exhaustive study has been realized to achieve the best performance algorithm taking into account different settings. In the second part of the project, Detectron2 has been used, an advanced machine learning program that performs object detection. We have worked with this program and executed a study of the motion of moving airplanes, implementing a new method to track objects given a set of images extracted from a given video.


Data de generació 26/01/2021