Multi-Object association with belief functions: the dual decision algorithm

Jean-Philippe Lauffenburger (1) , Jérémie Daniel (1) , Osamah Saif (2)

1 IRIMAS - Institut de Recherche en Informatique Mathématiques Automatique Signal
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

In intelligent vehicles applications, Multi-Target Tracking (MTT) is usually done considering imperfect sensors' data. As belief functions and particularly the Transferable Belief Model (TBM) help to model these imperfections, it represents an interesting framework for MTT. To carry out target tracking, the system must be able to de fine at time k the relations between the detected objects (targets) and the already known ones (tracks). The correlation step is called Multi-Object Association (MOA). This paper tackles the problem of TBM-MOA decision making. The aim is to present a methodology and measures for the management of associations in presence of conflicting sensors' measures. A new algorithm based on a single association matrix gathering all the knowledge of the targets-to-tracks associations is presented. It provides a semantic to avoid the aforementioned di culties. Simulation results of an obstacle detection application show the advantages of the proposed solution.

Mots clés: Transferable Belief Model; data fusion; object association; intelligent vehicles

Domaines: Automatique / Robotique; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Jean-Philippe Lauffenburger, Jérémie Daniel, Osamah Saif. Multi-Object association with belief functions: the dual decision algorithm. Advances in Control and Automation Theory for Transportation Applications, Sep 2013, Istanbul, Turkey. pp.TuDT2. ⟨hal-00868778⟩