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2020-11-04 - Colloque/Article dans les actes avec comité de lecture - Anglais - 6 page(s)

Bornia Jemai, Frihida Ali, Debauche Olivier , Mahmoudi Sidi , Manneback Pierre , "Deep Learning and Tensorflow for Tracking People’s Movements in a Video" in 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, Marrakech, Maroc, 2020

  • Codes CREF : Sciences agronomiques (DI3600), Informatique générale (DI1162)
  • Unités de recherche UMONS : Informatique, Logiciel et Intelligence artificielle (F114)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
Texte intégral :

Abstract(s) :

(Anglais) With the advent of the digital age and more specifically videos, a huge amount of data is produced every day such as television archiving, video surveillance, etc. Faced with the need to keep control over this content, in terms of data analysis, classification, accurate AI (Artificial Intelligence) algorithms are required to perform this task efficiently and quickly. In this paper, we propose an approach for movement analysis from video sequences using deep learning technologies. The proposed approach splits video in set of images, detects objects/entities present in these images and stores their descriptions into a standard XML file. As result, we provide a Deep Learning algorithm using TensorFlow for tracking motion and animated entities in video sequences.


Mots-clés :
  • (Anglais) TensorFlow
  • (Anglais) object tacking
  • (Anglais) deep learning
  • (Anglais) video
  • (Anglais) motion tracking