Visual Tracking via Particle Filtering on the Affine Group
- 14 August 2009
- journal article
- Published by SAGE Publications in The International Journal of Robotics Research
- Vol. 29 (2-3), 198-217
- https://doi.org/10.1177/0278364909345167
Abstract
We present a particle filtering algorithm for visual tracking, in which the state equations for the object motion evolve on the two-dimensional affine group. We first formulate, in a coordinate-invariant and geometrically meaningful way, particle filtering on the affine group that allows for combined state—covariance estimation. Measurement likelihoods are also calculated from the image covariance descriptors using incremental principal geodesic analysis, a generalization of principal component analysis to curved spaces. Comparative visual tracking studies demonstrate the increased robustness of our tracking algorithm.Keywords
This publication has 23 references indexed in Scilit:
- Monte Carlo filtering on Lie groupsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Riemannian geometry for the statistical analysis of diffusion tensor dataSignal Processing, 2007
- Geometric Means in a Novel Vector Space Structure on Symmetric Positive‐Definite MatricesSIAM Journal on Matrix Analysis and Applications, 2007
- Kernel particle filter for visual trackingIEEE Signal Processing Letters, 2005
- Principal Geodesic Analysis for the Study of Nonlinear Statistics of ShapeIEEE Transactions on Medical Imaging, 2004
- A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Transactions on Signal Processing, 2002
- Sequential Monte Carlo Methods in PracticePublished by Springer Science and Business Media LLC ,2001
- Self-Organizing Time Series ModelPublished by Springer Science and Business Media LLC ,2001
- CONDENSATION—Conditional Density Propagation for Visual TrackingInternational Journal of Computer Vision, 1998
- Novel approach to nonlinear/non-Gaussian Bayesian state estimationIEE Proceedings F Radar and Signal Processing, 1993