Hierarchical and conditional combination of belief functions induced by visual tracking

  • Authors:

  • John Klein;Christèle Lecomte;Pierre Miché

  • Affiliations:

  • LAGIS, University of Lille1, UMR CNRS 8146, France;LAGIS, University of Lille1, UMR CNRS 8146, France;LITIS, University of Rouen, EA 4108, France

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2010

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Abstract

In visual tracking, sources of information are often disrupted and deliver imprecise or unreliable data leading to major data fusion issues. In the Dempster-Shafer framework, such issues can be addressed by attempting to design robust combination rules. Instead of introducing another rule, we propose to use existing ones as part of a hierarchical and conditional combination scheme. The sources are represented by mass functions which are analysed and labelled regarding unreliability and imprecision. This conditional step divides the problem into specific sub-problems. In each of these sub-problems, the number of constraints is reduced and an appropriate rule is selected and applied. Two functions are thus obtained and analysed, allowing another rule to be chosen for a second (and final) fusion level. This approach provides a fast and robust way to combine disrupted sources using contextual information brought by a particle filter. Our experiments demonstrate its efficiency on several visual tracking situations.