ATTENTION/WARNING - NE PAS DÉPOSER ICI/DO NOT SUBMIT HERE

Ceci est la version de TEST de DIAL.mem. Veuillez ne pas soumettre votre mémoire sur ce site mais bien à l'URL suivante: 'https://thesis.dial.uclouvain.be'.
This is the TEST version of DIAL.mem. Please use the following URL to submit your master thesis: 'https://thesis.dial.uclouvain.be'.
 

Smart crossroad surveillance in urban areas

Defrenne, Daniel
(2021)

Files

Defrenne_32381500_2020.pdf
  • Open access
  • Adobe PDF
  • 3.43 MB
Download

Details

Supervisors
Macq, Benoît
Faculty
Ecole polytechnique de Louvain
Degree label
Master [120] : ingénieur civil électricien, à finalité spécialisée
Abstract
Videos are taking more and more place in the type of data we store on our phones, laptops and other electronics. Technology is advanced to a point we are able to detect, recognise and track vehicles, people or anything that come to our mind. All these algorithms are based on decoded videos. What if it was possible to use the encoded video format and do exactly the same ? Could it perform better ? What about privacy of the surroundings ? The encoded format is quite different than the decoded format, creating a new challenge. The goal of this thesis is to solve the problem, to see if encoded videos are one solution that could lead to more privacy.