Robust Object Tracking using Particle Filters and Multi-Region Mean Shift
Paper in proceedings, 2009

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.

joint mean shift and particle filters

object tracking

particle filters

multi- mode anisotropic mean shift

Author

Andrew Backhouse

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Zulfiqar Hasan Khan

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 5879 11-

Subject Categories

Computer Engineering

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1007/978-3-642-10467-1_34

ISBN

978-364210466-4

More information

Created

10/6/2017