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Joint particle filters and multi-mode anisotropic mean shift for robust tracking of video objects with partitioned areas

Zulfiqar H. Khan (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Andrew Backhouse (Institutionen för signaler och system, Signalbehandling)
IEEE international conf. on image processing (ICIP 2009) p. 4077-4080. (2009)
[Konferensbidrag, refereegranskat]

We propose a novel scheme that jointly employs anisotropic mean shift and particle filters for tracking moving objects from video. The proposed anisotropic mean shift, that is applied to partitioned areas in a candidate object bounding box whose parameters (center, width, height and orientation) are adjusted during the mean shift iterations, seeks multiple local modes in spatial-kernel weighted color histograms. By using a Gaussian distributed Bhattacharyya distance as the likelihood and mean shift updated parameters as the state vector, particle filters become more efficient in terms of tracking using a small number of particles (<20). The combined scheme is able to maintain the merits of both methods. Experiments conducted on videos containing deformable objects with long-term partial occlusions and intersections have shown robust tracking performance. Comparisons with two existing methods have been made which showed marked improvement in terms of robustness to occlusions, tightness and accuracy of tracked box, and tracking drift.

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



Denna post skapades 2009-11-27. Senast ändrad 2010-09-07.
CPL Pubid: 102346

 

Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Datalogi
Bildanalys
Signalbehandling

Chalmers infrastruktur

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