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Robust Object Tracking using Particle Filters and Multi-Region Mean Shift

Andrew Backhouse (Institutionen för signaler och system, Signalbehandling) ; Zulfiqar H. Khan (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
LNCS (for 10th IEEE Parcific-Rim conf. on Multimedia PCM '09) Vol. 5879 (2009), p. 11.
[Konferensbidrag, refereegranskat]

In this paper, we introduce a novel algorithm which builds upon the combined anisotropic mean-shift and particle filter framework. The anisotropic mean-shift with 5 degrees of freedom, is extended to work on a partition of the object into concentric rings. This adds spatial information to the description of the object which makes the algorithm more resilient to occlusion and less susceptible to confusion with objects having similar color densities. Experiments conducted on videos containing deformable objects with long-term partial occlusion (or, short-term full occlusion) and intersection have shown robust tracking performance, especially in tracking objects with long term partial occlusion, short term full occlusion, close color background clutter, severe object deformation and fast changing motion. Comparisons with two existing methods have shown marked improvement in terms of robustness to occlusions, tightness and accuracy of tracked box, and tracking drifts.

Nyckelord: joint mean shift and particle filters, object tracking, multi- mode anisotropic mean shift, particle filters



Denna post skapades 2009-11-27.
CPL Pubid: 102357

 

Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Datorteknik
Bildanalys
Signalbehandling

Chalmers infrastruktur