Feedback

Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
VIEW 75 | DOWNLOAD 11

Deep learning for object detection in video streams

Download
Lentz, Charles ULiège
Promotor(s) : Louppe, Gilles ULiège
Date of defense : 26-Jun-2019/27-Jun-2019 • Permalink : http://hdl.handle.net/2268.2/6736
Details
Title : Deep learning for object detection in video streams
Translated title : [fr] Apprentissage approfondi pour la détection d'objets dans les flux vidéo
Author : Lentz, Charles ULiège
Date of defense  : 26-Jun-2019/27-Jun-2019
Advisor(s) : Louppe, Gilles ULiège
Committee's member(s) : Van Droogenbroeck, Marc ULiège
Marée, Raphaël ULiège
Moreau, Ghislain 
Language : English
Number of pages : 91
Keywords : [fr] Deep Learning, Computer Vision, Sensor
Discipline(s) : Engineering, computing & technology > Computer science
Research unit : BEA Sensors
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil électricien, à finalité spécialisée en "signal processing and intelligent robotics"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[fr] ”Artificial intelligence is no match for natural stupidity” is the famous quote of the
brilliant Albert Einstein. Born in the fifties, artificial intelligence was already a source
of admiration. It has undergone a spectacular evolution in recent years and continues to
develop following technological and computer advances. Nowadays, artificial intelligence
plays a fundamental role in research and is ubiquitous in our daily lives.
The detection of objects in a video stream with deep learning having become accessible
and efficient thanks to the more and more performant processor, this principle will be
adapted in order to be able to use it as a door opening sensor. This thesis was conducted
in a sensor company called BEA. It is a leading manufacturer of sensing solutions for
automatic doors systems. The company was founded in 1965 and its headquarters are
located in Li`ege, Belgium.
As artificial intelligence is making great strides in our technologies, the company has
decided to do some experimentations with this process. The detection and classification
of objects in a video stream through deep learning is the main subject of this work. The
use of it in real time is a big challenge because it was necessary to handle the use of a
neural network accelerator while ensuring that the model used was not too consequent.
Despite this constraint, it was still necessary that the precision and the accuracy were
sufficient for the application.
In order to be independent of the position of the camera and its orientation for future
applications, a ground projection algorithm has been implemented. Then, to improve the
feasibility of the process, a Kalman filter will be integrated into the detected objects and
a tracking will be assigned to it.
Subsequently, open decision-making and people counting were implemented and all
was adapted to the Raspberry Pi 3 B + embedded system.
Finally, various evaluation tests were carried out to demonstrate the system’s fidelity
and its promising potential.


File(s)

Document(s)

File
Access Report_Charles_Lentz_s140801_Master_Thesis.pdf
Description: Master's Thesis Report | BEA sensors | Charles Lentz
Size: 9.16 MB
Format: Adobe PDF
File
Access Resume_Charles_Lentz_s140801_Master_Thesis.pdf
Description: Master's Thesis Resume | BEA sensors | Charles Lentz
Size: 159.57 kB
Format: Adobe PDF

Author

  • Lentz, Charles ULiège Université de Liège > Master ingé. civ. électr., à fin.

Promotor(s)

Committee's member(s)

  • Van Droogenbroeck, Marc ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
    ORBi View his publications on ORBi
  • Marée, Raphaël ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
    ORBi View his publications on ORBi
  • Moreau, Ghislain
  • Total number of views 75
  • Total number of downloads 11










All documents available on MatheO are protected by copyright and subject to the usual rules for fair use.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.