Joint particle filters and multi-mode anisotropic mean shift for robust tracking of video objects with partitioned areas
Paper i proceeding, 2009

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.

multi-mode anisotropic mean shift

object tracking

particle filters

joint mean shift and particle filters

Författare

Zulfiqar Hasan Khan

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Andrew Backhouse

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

IEEE international conf. on image processing (ICIP 2009)

4077-4080

Ämneskategorier

Signalbehandling

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)