Correlation-based particle filter for 3D object tracking

Article type: Research Article

Authors: Noyer, Jean-Charles^{; *} | Lanvin, Patrick | Benjelloun, Mohammed

Affiliations: Université du Littoral Côte d'Opale, Laboratoire d'Analyse des Systèmes du Littoral, Calais Cedex, France

Correspondence: [*] Corresponding author: Prof. Jean-Charles Noyer, Université du Littoral Côte d'Opale, Laboratoire d'Analyse des Systèmes du Littoral, 50 Rue F. Buisson, B.P. 699, 62228 Calais Cedex, France. Tel.: +33 3 21 46 56 62; Fax: +33 3 21 46 06 86; E-mail: [email protected].

Abstract: This manuscript deals with the problem of 3D object tracking in a multisensor framework. The object is here described by a CAD model. It avoids any image preprocessing that leads, generally, to loss of information. We develop a particle filtering method [6] that we call “correlation-based particle filter” (CBPF) to solve this non-linear estimation problem. The new proposed approach is applied to synthetic and real image sequences of complex 3D moving objects. The originality of this work consists of developing a centralized fusion method that uses, in an optimal way, the measurements delivered by the sensors. In order to optimally using the sensor outcomes, a centralized fusion approach is proposed. The method can jointly estimate 3D pose/motion parameters and track the object in the 3D domain, while many works have been developed in the image plane. Finally, we should mention that the method is not limited in terms of object structure and motion.

DOI: 10.3233/ICA-2009-0311

Journal: Integrated Computer-Aided Engineering, vol. 16, no. 2, pp. 165-177, 2009