Laboratoire de Génie Informatique et d’Automatique de l’Artois

Ph.D. thesis of Samir HACHOUR

Multi-object tracking and classification : contributions with belief functions theory

Starting date: 1 December 2011
Keywords: Multi-object tracking and classification, data assignment, belief functions, multi-sensor data fusion

This thesis deals with multi-objet tracking and classification problem. It was shown that belieffunctions allow the results of classical Bayesian methods to be improved. In particular, a recentapproach dedicated to a single object classification which is extended to multi-object framework. Itwas shown that detected observations to known objects assignment is a fundamental issue in multiobjecttracking and classification solutions. New assignment solutions based on belief functionsare proposed in this thesis, they are shown to be more robust than the other credal solutions fromrecent literature. Finally, the issue of multi-sensor classification that requires a second phase ofassignment is addressed. In the latter case, two different multi-sensor architectures are proposed, aso-called centralized one and another said distributed. Many comparisons illustrate the importanceof this work, in both situations of constant and changing objects classes.

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Defense took place the 05/06/2015 am30 10:00 Prestige room - FSA - Béthune


  • Président Didier MAQUIN Université de Lorraine
  • Rapporteur Thierry DENOEUX Université de Technologie de Compiègne
  • Rapporteur Arnaud MARTIN Université de Rennes 1
  • Examinateur Jean-Charles NOYER Université du Littoral côte d'Opale
  • Directeur François DELMOTTE Université d'Artois
  • Co-supervisor David MERCIER Université d'Artois