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Combining Foreground / Background Feature Points and Anisotropic Mean Shift For Enhanced Visual Object Tracking

Sebastian Haner (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
20th International Conf. Pattern Recognition (ICPR 2010), 23-26 August, 2010, Istanbul, Turkey (1051-4651). p. 3488-3491. (2010)
[Konferensbidrag, refereegranskat]

This paper proposes a novel visual object tracking scheme, exploiting both local point feature correspondences and global object appearance using the anisotropic mean shift tracker. Using a RANSAC cost function incorporating the mean shift motion estimate, motion smoothness and complexity terms, an optimal feature point set for motion estimation is found even when a high proportion of outliers is presented. The tracker dynamically maintains sets of both foreground and background features, the latter providing information on object occlusions. The mean shift motion estimate is further used to guide the inclusion of new point features in the object model. Our experiments on videos containing long term partial occlusions, object intersections and cluttered or close color distributed background have shown more stable and robust tracking performance in comparison to three existing methods.

Nyckelord: Visual object tracking, video surveillance, mean shift, SIFT, SURF, RANSAC, dynamic maintenance



Denna post skapades 2010-05-24. Senast ändrad 2010-11-23.
CPL Pubid: 121830

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Bildanalys

Chalmers infrastruktur